Efficiency Scores Research Articles

Costless CO2 emissions abatement through improved government effectiveness

ABSTRACT The withdrawal of Canada, Japan, New Zealand, and Russia from the Kyoto Protocol, the non-ratification of the United States at the onset, and the exemption given to China and India indicate that five out of the ten largest economies in the world, accounting for approximately 50% of the world GDP (as of 2021), have turned away from the Kyoto Protocol. This apprehension can plausibly be explained by the potential loss of productivity resulting from abatement of CO2 emissions. Therefore, this study examines the marginal effect of CO2 emission abatement on technical efficiency and how it can be moderated by government effectiveness. The following are the findings of this study: (1) inclusion of the pollution effect in the modelling of technical efficiency is necessary as it significantly changes the technical efficiency score ranking; (2) the opportunity cost of CO2 emissions abatement exists in the form of productivity loss but is significantly moderated by government effectiveness. These findings are important as they aid policymakers in mapping out a strategy for the desired costless abatement of CO2 emissions.

Intertemporal comparison of cost and technical efficiencies using a base period approach for the Korean rice industry

AbstractObjectives of our study are to develop a procedure for intertemporal comparison of both technical and cost efficiency and estimate farm efficiency for the Korean rice industry from 2003 to 2017. The newly developed base‐year procedure excludes frontier shift and price effects from the standard procedure for intertemporal comparison. An adjusted central limit theorem for sample T‐tests is applied to avoid potential bias from efficiency scores by the Data Envelope Analysis. Our empirical results show that the two procedures yield different scores and trends. The standard approach indicates declining efficiency, while the base‐year method shows overall improvement in farm efficiency.

Evaluating Public Spending Efficiency and its Determinants Towards Social Outcomes: Empirical Evidence from Indian States

The topic of public expenditure efficiency has gained a key position in the spheres of public policy goals. This article tries estimating the efficiency of public spending on health and education sectors across Indian states from 1990–1991 to 2016–2017. In input–output linkage, we have computed efficiency using a slacks-based measure model of data envelopment analysis through different orientations. The study also captures the externalities of public spending efficiency with some exogenous factors. It also suggests that these exogenous factors (such as good governance, per capita income and proportion of literates in the household) have significantly improved the output efficiency score of the health as well as the education sectors across Indian states. JEL Codes: H51, H52, H21, E61

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

On the fourth-order hybrid beta polynomial kernels in kernel density estimation

This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.

Estimating cost efficiency and sources of inefficiency in paddy farming: A study in Vietnam’s Mekong Delta

The misuse of chemical fertilizers, pesticides and herbicides in rice cultivation is leading to low-quality outputs, high production costs, health issues and environmental problems (e.g., degraded soil quality, water pollution and increasing greenhouse gases). The efficient use of production inputs would be a feasible way to mitigate these issues. This paper employed a true random-effects model to measure cost efficiency and investigate the factors affecting cost inefficiency among Vietnamese rice producers. This study used the surveyed data of 350 rice households collected in the Mekong Delta, Vietnam. The findings of this research show that the mean cost efficiency score is 0.92 with a wide variation (0.26 – 0.99). This study indicates that there is still potential for inefficient rice producers to save production costs by improving their cost inefficiency. The study also reveals a positive relationship between cost inefficiency and farm size, natural disasters and rice diseases. This suggests that as farms grow, natural disasters and rice diseases become more prevalent and rice producers become increasingly incapable of managing input costs. This study suggests that supportive policies should focus on improving rice farmers’ skills to manage production inputs and deal with rice diseases and natural disasters to minimize rice production costs.

CIPP Model Approach to School-Based Management Program Evaluation

This study is an evaluative study using the CIPP model to evaluate the effectiveness of implementing the School-based Management program at the 1 Sukasada State High School. The subjects involved 70 respondents, including the head of the school, teachers, entrepreneurs, students, and school committees. Data processing is performed in a descriptive quantitative manner with simple statistical analysis and tabulation, following the National Department of Education standards. The results showed a context component score of 2,973, an input of 3,604, a process of 2,892, and a product (output and outcomes) of 2,495. The results of the observations show that the conditions of the community and the potential of the area are very supportive of the development of schools. However, of the 483 alumni, only 230 (46.75%) were traceable, with 27.39% working according to competence and 20.87% continuing education. Low output quality is affected by the efficiency of processes, contexts, and inputs, resulting in low school performance efficiency scores. Thus, the effectiveness of the implementation of School-based Management in the State High School 1 Sukasada was assessed as unsatisfactory. It is recommended that managers improve planning, consider market needs, and improve the effectiveness of implementation and evaluation of School-Based Management programs in the school.

Multicast Routing Based on Data Envelopment Analysis and Markovian Decision Processes for Multimodal Transportation

In the context of Iraq’s evolving transportation landscape and the strategic implications of the Belt and Road Initiative, this study pioneers a comprehensive framework for optimizing multimodal transportation systems. The study implemented a decision-making framework for multimodal transportation, combining data envelopment analysis (DEA) efficiency scores and a Markov decision process (MDP) to optimize transportation strategies. The DEA scores captured decision-making unit (DMU) performance in various aspects, while the MDP rewards facilitated strategic mode selection, promoting efficiency, cost-effectiveness, and environmental considerations. Although our method incurs a total cost approximately 29% higher than MRMQoS, it delivers a nearly 26% reduction in delay compared to MCSTM. Despite MRMQoS yielding an 8.3% higher profit than our approach, our proposed scheme exhibits an 11.7% higher profit compared to MCSTM. In terms of computational time, our method achieves an average CPU time positioned between MCSTM and MRMQoS, with MCSTM showing about 1.6% better CPU time than our approach, while our method displays a 9.5% improvement in computational time compared to MRMQoS. Additionally, concerning CO2 emissions, the proposed model consistently outperforms other models across various network sizes. The percentage decrease in CO2 emissions achieved by the proposed model is 7.26% and 31.25% when compared against MRMQoS and MCSTM for a network size of 25, respectively.

The Effects of Body-Worn Cameras on Police Efficiency: A Study of Local Police Agencies in the United States

Research on body-worn cameras (BWCs) has burgeoned in recent years. However, much of this research has not examined the effects of this technology on police performance. In this study, we created a novel dataset on police organizational inputs and outputs and assessed the effects that BWCs have on police efficiency. Using a multiple-stage approach, in the first stage, we estimated the efficiency of local police agencies using a robust order- m efficiency model. In the second stage, we assessed the effects of BWCs using a range of matching estimators and an instrumental variable (IV) model. The first-stage results show that police agencies could improve their efficiency by 2% (from a mean efficiency score of 0.98 to 1). The second-stage matching and IV estimates suggest BWC effects on efficiency range between four and six percentage points in our preferred model. The introduction of this significant technological advance has brought many benefits to police agencies. Our findings show that BWCs can help improve police efficiency, yielding significant cost savings to law enforcement.

Poor Sleep Quality in Jamaican Adults With Sickle Cell Disease: Prevalence, Risk Factors, and Association With Quality of Life

Poor sleep and chronic illnesses have a bidirectional relationship where presence of one can worsen the other. Sickle cell disease (SCD) is associated with significant morbidity and early mortality. In this study, we examine sleep quality, its predictors, and its association with quality of life in Jamaican adults with SCD. This cross-sectional study evaluated 177 well adult SCD patients for sleep quality using The Pittsburgh Sleep Quality Index (PSQI) and quality of life using the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me). Multiple linear regression models examined the predictors of poor sleep quality. The mean global PSQI score was 6.9 (SD 4.2) with 56.5% having poor sleep quality. Women had significantly worse scores for sleep efficiency (p 0.005), sleep latency (p 0.03) and higher use of sleeping medications (p 0.02). Those overweight/obese had significantly worse subjective sleep quality (p 0.001) and sleep efficiency (p 0.05). In multivariate regression analysis, overweight individuals had poorer sleep quality (OR: 2.9; 95% C.I.: 1.07, 7.88) than those with normal weight whereas those unemployed and looking for a job had lower prevalence of poor sleep quality (OR 0.2; 95% C.I.: 0.05, 0.77) compared to employed individuals. Participants with good sleep quality had significantly better functioning in all 5 domains of the ASCQ-Me. In conclusion, persons with SCD who are overweight or obese are at increased risk of poor sleep which can negatively affect quality of life. Patient populations and healthcare providers will need to manage the emerging burden of overweight/obesity.

How Comprehensive and Efficient Are Patient-Reported Outcome Measures for Individuals with Lower Extremity Amputation Undergoing Implantation of Osseointegrated Bone Anchored Limbs?

» Patient-reported outcome measures (PROMs) are essential for measuring quality and functional outcomes after implantation of osseointegrated bone anchored limbs for patients with lower extremity amputation.» Using a novel assessment criterion with 8 domains, this study assessed all commonly used PROMs for their efficiency and comprehensiveness.» Comprehensiveness was scored according to the presence or absence of PROM questions related to these 8 domains (maximum score = 60): mobility (15 items), prosthesis (14 items), pain (10 items), psychosocial status (10 items), independence/self-care (4 items), quality of life/satisfaction (4 items), osseoperception (1 item), general information (1 item), and vitality (1 item).» The efficiency scores were calculated by dividing the comprehensiveness score by the total number of questions answered by the patients with higher scores being deemed more efficient.» The most comprehensive PROMs were Orthotics and Prosthetics User's Survey-Lower Extremity Functional Status (OPUS-LEFS) (score = 36), Prosthesis Evaluation Questionnaire (PEQ) (score = 31), and Questionnaire for Persons with a Transfemoral Amputation (score = 27).» The most efficient PROMs were the OPUS-LEFS (score = 1.8) and European Quality of Life (score = 1.4).

A flexible stochastic production frontier model with panel data

SummaryWe propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. To avoid potential misspecification and/or “wrong skew problem” due to distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference‐based estimator for parameters characterizing the conditional mean of the inefficiency term, a profile series estimator, and a kernel‐based one‐step backfitting estimator for the frontier to facilitate inference. We establish their asymptotic properties and show that each component in the frontier estimated by the kernel‐based backfitting has the same asymptotic distribution as the one estimated with the true knowledge on the other components in the frontier (i.e., the oracle property). Through a Monte Carlo study, we demonstrate that the proposed estimators perform well in finite samples. Utilizing a panel of Chinese firm‐level data in 2000–2006, we apply our method to estimate the frontier and efficiency scores and conclude that export plays a significant role in reducing the efficiency of firms.

Assessing the Drivers of Sustained Agricultural Economic Development in China: Agricultural Productivity and Poverty Reduction Efficiency

Poverty eradication is a crucial element of SDG 1. Whether the financial resources invested by the Government provide a critical impetus for deeply impoverished rural areas needs to be studied by quantitative analysis. Therefore, this study presents a theoretical analytical framework for agricultural production–government poverty reduction. It divides the poverty reduction process into two stages, agricultural production and poverty reduction, from the perspective of sustainable agricultural development. The comprehensive measurement and spatio-temporal evolution analysis of China’s agricultural production and poverty reduction efficiency are conducted using a novel dynamic two-stage DEA model, which incorporates non-expected factors. The study found that (1) China’s agricultural production and poverty reduction efficiency exhibit overall poor performance, characterized by two poles of differentiation. (2) The agricultural production efficiency score is higher in the northern region than in the southern region, while the poverty reduction efficiency score is higher in the eastern region compared to the western region. (3) The coupling and coordination between China’s production efficiency and poverty reduction efficiency are inadequate.

Water quality prediction based on sparse dataset using enhanced machine learning

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

Assessing airport efficiency in the Sakha Republic: A DEA-SBM analysis

In the Sakha Republic, aviation serves as the sole mode of year-round transportation, which is crucial for overcoming logistical challenges in remote areas of the region. This study utilizes DEA-SBM analysis to assess the efficiency of 21 airports in addressing issues within the Sakha Republic’s airport infrastructure. The study revealed that six airports achieved the highest efficiency score: Batagay Airport, Chulman Airport, Deputatsky Airport, Ust-Kuyga Airport, Ust-Nera Airport, and Yakutsk Airport. The findings highlight the potential of DEA-SBM analysis for airport operators and managers to evaluate input and output levels for enhanced operational efficiency.

Comparative Network Efficiency Analysis of the Airlines in Turkey After Deregulation

Deregulation has significantly developed the civil air transport industry in an ever-globalizing world. Even though deregulation has caused significant structural transformations in airline companies, the effect of deregulation effect on the production, marketing efficiency, and competitiveness of airline carriers worldwide, especially in Turkey, has not been fully revealed yet. Therefore, this study aims to analyze the efficiency of Turkish air carriers after the deregulation process in Turkish civil aviation by dividing the efficiency into production and market efficiency. Production and marketing efficiencies of airlines were estimated using the window network data envelopment analysis methodology. Efficiency analysis results showed production efficiency at 0.887, marketing efficiency at 0.764, and system efficiency at 0.796. Results also indicate that low-cost airlines have a higher production efficiency score (0.918) than full-service airlines (0.825). In comparison, the marketing efficiency of full-service airlines (0.879) is higher than that of low-cost carriers (0.708). The study determined that the system efficiency does not change according to the business model. The system efficiency score of the full-service provider airlines with a larger market share is higher and more balanced. The close and dynamic monitoring of the air transport market and the continuation of operations under a business model incorporating an appropriate marketing mix will increase the marketing efficiency of the airlines.

Analysis and integration of mixed method in efficiency studies: Best practices and applications in the renewable energy sector

Mixed-method research is based on multiple methodological strategies, prioritizing high technical rigor with scientific solidity and contributing to practical improvements. Therefore, this study aimed to present a mixed method with analysis and integration in efficiency studies in the renewable energy sector through the relationship between productivity and optimization of results. This mixed-method methodology was proposed in: (i) theoretical mathematical model, (ii) efficiency model (DEA, SFA), technique, definition of ranking and sampling criteria, (iii) variables that influence this efficiency, which will be the bases for research instrument applied at case studies (quanti-quali integration), and (iv) identification of best practices from case studies. The efficiency of each DMU established a ranking (scores), which was used as independent variable in the regression and identified the variables that impact on efficiency. These results enabled the creation of an instrument to identify the best practices that impacts on the efficiency score. Notably, this method can help stakeholders of the sugar-energy production chain achieve their efficiency through best practices generated. It has been applied in other economic sectors, such as education, banking, healthcare, and solid waste, and replication in different periods and countries using variables from public or private databases.•Develop stakeholders of sugar-energy production chain to achieve and improve their efficiency through best practices generated through production analysis.•It has been applied in other economic sectors, such as education, banking, healthcare and solid waste.•There are opportunities for replication in different periods and countries using variables from public or private databases.

Understanding energy performance in drinking water treatment plants using the efficiency analysis tree approach

Water treatment processes are known to consume substantial amounts of energy, making it crucial to understand their efficiency, drivers, and potential energy savings. In this study, we apply Efficiency Analysis Tree (EAT), which combines machine learning and linear programming techniques to assess the energy performance of 146 Chilean drinking water treatment plants (DWTPs) for 2020. Additionally, we utilize bootstrap regression techniques to examine the influence of operating characteristics on energy efficiency. The results indicate that the evaluated DWTPs exhibited poor energy performance, with an average energy efficiency score of 0.197. The estimated potential energy savings were found to be 0.005 kWh/m3. Several factors, such as the age of the facility, source of raw water, and treatment technology, were identified as significant drivers of energy efficiency in DWTPs. The insights gained from our study can be valuable for policymakers in making informed decisions regarding the adoption of practices that promote efficient and sustainable energy use within the water cycle.

Efficiency analysis and CO2 emission reduction strategies in the US forest sector: a data envelopment analysis approach

Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5–10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Despite the rapid growth of machine learning (ML) and its far-reaching applications in various fields such as healthcare, finance, and urban heat management, there are still some unresolved challenges in the field of climate change. Reliable subseasonal forecasts of summer temperatures would be a great benefit to society. Although numerical weather prediction (NWP) models are better at capturing relevant sources of predictability, such as temperatures, land, and sea surface conditions, the subseasonal potential is not fully exploited. One such challenge is accurate subseasonal temperature forecasting using cutting-edge ML technology. This study aims to assess and predict the changes in subseasonal temperature during the summer season (from March to June) in Senegal on 2-weeks time scales. Six ML techniques, including linear regression (LR), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent units (GRU), are used. The experiments utilize a multivariate approach by incorporating variables of the ERA-5 dataset from 1981 to 2022. The results compared all the performances of the methods to assess their overall effectiveness in forecasting air temperature (t2m) values over 2 weeks. Our analysis demonstrates that the GRU model outperforms the other ML models, achieving a Nash–Sutcliffe efficiency (NSE) score of 74.68% and a mean absolute percentage error (MAPE) of 2.51%. The GRU model effectively captures long-term dependencies and exhibits superior performance in temperature forecasting. Furthermore, a comparison between the observed and predicted values confirms the accuracy of the GRU model in aligning with actual temperature trends. Overall, this study contributes an impactful deep learning model to the field of subseasonal temperature forecasting in West Africa (Senegal), which offers local authorities the capability to anticipate climatic events and enact preventive measures accordingly.