Intersectional equity in Brazil's remote rural municipalities: the road to efficiency and effectiveness in local health systems.

Objective: The Brazilian remote rurality has been classified more reliably only recently, according to demographic density, proportion of urban population, and accessibility to urban centers. It comprises 5.8% of the municipalities, in nearly half of the states, with a population of 3,524,597 (1.85%). Remote rural localities (RRL) have reduced political/economic power, facing greater distances and barriers. Most health strategies are developed with the urban space in mind. We aim to understand how RRL are positioned concerning efficiency/effectiveness in health, compared to other urban-rural typologies of Brazilian localities, focusing on Primary Health Care (PHC), and its organizational models. Methods: We evaluated the efficiency and effectiveness of the organizational models using the health production model, from 2010-2019, gradually deepening the immersion into the RRL reality. We analyzed the human and financial resources dimensions, emphasizing teams, the results of PHC actions, and health levels. We used the fixed effects model and data envelopment analysis, cross-sectioned by intersectional inequities. We compared the Brazilian states with and without RRL, Brazilian municipalities according to rural-urban typologies, and RRL clusters. Results: Brazilian RRL states show superior resource/health efficiency through services utilization according to health needs. The remote rural typology demonstrated greater efficiency and effectiveness in health than the other typologies in the RRL states. The organizational models with the Family Health Strategy (FHS) teams and the Community Health Worker (CHW) visits played a key role, together with local per capita health expenditures and intergovernmental transfers. Thus, financial resources and health professionals are essential to achieve efficient/effective results in health services. Among the RRL, the Amazon region clusters stand out, denoting the importance of riverine and fluvial health teams, the proportion of diagnostic/treatment units in addition to the proportion of illiteracy and adolescent mothers along with the inequity of reaching high levels of schooling between gender/ethnicity. Conclusion: Hopefully, these elements might contribute to gains in efficiency and effectiveness, prioritizing the allocation of financial/human resources, mobile FHS teams, availability of local diagnosis/treatment, and basic sanitation. Finally, one should aim for equity of gender/ethnicity in income and education and, above all, of place, perceived in its entirety.

The efficiency evaluation of traditional Chinese medicine hospitals by data envelopment analysis in Zhengzhou, China.

Aim: This study aimed to evaluate the operational efficiency of traditional Chinese medicine (TCM) hospitals in China. Methods: Pearson's analysis was used to test the correlation between the input and output variables. Data envelopment analysis (DEA) was utilized to analyze the input and output variables of 16 TCM hospitals, and each hospital efficiency score was computed by Deap 2.1, assuming variable return to scale (VRS), which is an input-oriented model. t tests were conducted to confirm the significant difference of efficiency scores at the hospital level and by hospital type, and ANOVA was used to test for significant differences in efficiency scores according to hospitals' size. Results: The correlation coefficient of the input and output indicators was between 0.613 and 0.956 (p < 0.05). The difference in number of doctors (ND) and numbers of pharmacists (NP) were statistically significant (p < 0.05) at the hospital level. The mean efficiency scores for technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) in secondary TCM hospitals were 0.766, 0.919, and 0.838, respectively. Additionally, the lowest TE, PTE, and SE were 0.380, 0.426, and 0.380, respectively. Eight TCM hospitals in this study were DEA efficient, with an efficiency score of 1. There were no statistically significant differences in TE, PTE, and SE among hospital levels, hospital types or hospital sizes groups (p > 0.05). Conclusion: This study revealed that tertiary TCM hospitals had a greater level of efficiency than secondary TCM hospitals. In our study, 50% of TCM hospitals had inefficient management. Therefore, to activate the new development power of TCM hospitals, it is necessary to reform and improve the management system and mechanism of TCM hospitals, optimize the development environment of TCM hospitals and formulate development plans and measures based on local conditions.

Fiscal expenditure efficiency of China's coal to clean heating policy.

Government subsidies are the backbone of the large-scale Coal to Clean Heating Policy launched in China in 2017. We analyze data from 27 pilot cities from 2017 to 2019. We use a slacks-based measure model with undesirable outputs to evaluate the optimal design of central and local government subsidies under the policy. While the overall efficiency is high, provincial capitals and cities with lower economic development levels show less efficiency. Our findings suggest that distributing central government subsidies based solely on administrative levels is not the best way to achieve perfect efficiency. Additionally, perfect efficiency at the city level may take years to achieve. A sweeping phase-out of government subsidies could lead to undesirable policy outcomes. Content analysis of normative documents from the six best-performing cities and six least efficient cities indicates that better-formulated legal and policy documents may contribute to higher efficiency. As lessons for designing and evaluating large-scale energy policies, we recommend considering 17 factors that could impact subsidy efficiency. We also suggest implementing a progressive phase-out schedule as a measure of policy efficiency and identifying factors that could ensure the long-term success of nationally implemented government subsidies.

Spatial effects of township health centers' health resource allocation efficiency in China.

Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Comparative analysis of healthcare systems in the Horn of Africa and the evaluation of Türkiye's diplomatic health studies conducted in Somalia.

This study examines the effectiveness of the countries' health systems in the Horn of Africa region. It also investigates the perspectives of actors who have played an active role in health affairs in Somalia carried out by Türkiye. Using the Data Envelopment Analysis and Malmquist Total Factor Efficiency Analysis, we investigated the effectiveness of the health systems and improvements made throughout the years. In the countries of interest, efficiency levels and average total factor productivity showed positive and/or negative trends between 2000 and 2020. Kenya showed a marked performance in achieving improved average total factor productivity thanks to the effective use of current technology in health, success in integrating new technologies into the health system, and a high potential to produce more output despite insufficient existing inputs. The remaining countries lagged behind in improving their production factors. Since 2014, Türkiye has provided health services in Somalia through health diplomacy and conducted medical examinations for numerous patients in a well-equipped hospital.

Cette étude examine l'efficacité des systèmes de santé des pays de la région de la Corne de l'Afrique. Il étudie également les perspectives des acteurs qui ont joué un rôle actif dans les affaires de santé en Somalie menées par Türkiye. En utilisant l'analyse de l'enveloppe des données et l'analyses d'efficacité des facteurs totales de Malmquist, nous avons étudié l'efficience des systèmes de santé et les améliorations apportées au cours des années. Dans les pays intéressés, les niveaux d'efficacité et la productivité totale moyenne du facteur ont montré des tendances positives et/ou négatives entre 2000 et 2020. Le Kenya a fait preuve d'une performance marquée dans l'amélioration de la productivité totale moyenne du facteur grâce à l'utilisation efficace de la technologie actuelle dans le domaine de la santé, au succès de l'intégration de nouvelles technologies dans le système de santé et au potentiel élevé de produire plus de produits malgré l'insuffisance des produits existants. Les autres pays sont en retard dans l'amélioration de leurs facteurs de production. Depuis 2014, Türkiye a fourni des services de santé en Somalie par le biais de la diplomatie de santé et a effectué des examens médicaux pour de nombreux patients dans un hôpital bien équipé.

Embedding Water-Energy-Food nexus and circularity assessment for organization benchmarking: A case study for dairy farms.

The Water-Energy-Food (WEF) nexus approach is increasingly being used for supporting a transition to sustainable development, with initiatives involving the concept of circular economy (CE). In the agricultural sector in particular, assessing this nexus is crucial to ensure food security, control the consumption of key resources such as water and energy, as well as measure atmospheric emissions linked to climate change. This manuscript aims to propose a novel approach by coupling the WEF nexus with a circularity indicator, seeking to capture in a single index (the WEF+CEi) both performances in a sample of companies. The novel approach is applied to 30 dairy farms located in Galicia (NW Spain) to benchmark them in a holistic manner. To do this, the WEF nexus of each farm was represented through the following indicators: carbon footprint, water footprint, energy footprint, and food productivity. In addition, the percentage of circularity for each farm, and for the agro-industrial cooperative was measured thanks to the application of a circularity tool in percentage terms. Finally, the WEF+CEi indicator was obtained using the multicriteria mathematical tool of Data Envelopment Analysis (DEA). The results show that without considering the agro-industrial cooperative, the system is 51 % circular. On the other hand, considering the farms and the cooperative, the system goes up to 80 % of circularity. Finally, the proposed approach can support decision-making and provide insights for producers and stakeholders in the area.

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks.

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study's findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

Assessing the spatial-temporal environmental efficiency of global construction sector.

The extensive waste emissions and energy consumption in the construction industry has spurred calls for sustainable development reform. Identifying valuable development paradigms through evaluating the spatial-temporal environmental efficiency (EE) of the construction sector offers an effective avenue for practice guidance. However, a comprehensive and detailed global analysis of the construction sector's EE remains lacking. This study applies the super-slack-based measure (Super-SBM) model to assess EE of the construction sector across 112 countries from 2006 to 2016. The results reveal relatively low overall EE performance of global construction sector, with notable disparities among countries and improvements were observed in certain years. Singapore, Malta, and Paraguay emerge as top performers, while Moldova, Swaziland, and Iceland rank at the bottom. The findings highlight significantly better EE in European and coastal Asian countries compared to inland Asian and African nations. Using the Tobit model, we identify that industrial structure, trade dependency, international cooperation, energy intensity, and geographic factors significantly influence EE, with varying impacts across countries of different income levels. These findings provide a comprehensive overview of the global construction sector's environmental efficiency, emphasizing the urgent need for increased awareness and the adoption of sustainable practices. The study offers practical policy implications, advocating for targeted strategies to balance economic development with environmental protection.

Improving energy-related efficiency towards SDG7 in China: What role does energy poverty play?

The improvement of energy-related efficiency has promoted energy-sustainable development in China; however, it may be inhibited by energy poverty. This paper constructs an index system to measure China's energy poverty. Subsequently, we evaluate energy-related efficiency towards SDG7 comprised of energy production efficiency and energy sustainable utilization efficiency using a dynamic two-stage data envelopment analysis model. Furthermore, we adopt fixed effect models to quantify the impact of energy poverty on improving energy-related efficiency and the underlying mechanism. The findings indicate that: (1) China's energy poverty situation gradually improves from 2011 to 2020. The energy-related system's performance is medium due to the poor performance in its energy production stage in 30 provinces during 2011-2020. Energy-related efficiency is greater in areas with a higher response to SDG7. (2) The impact of energy poverty on improving energy-related efficiency is significantly negative. Addressing energy poverty significantly improves energy-related efficiency in regions characterized by a high energy poverty index and low energy-related efficiency. However, it bears no impact in regions with a low energy poverty index and high energy-related efficiency. (3) The moderating effect of technological innovation effectively improves energy-related efficiency, whereas the marketization level has an inverse effect. Consequently, this paper suggests several policy implications.

The efficiency of clinical laboratories: the case of Kerman province.

BACKGROUND: Medical diagnostic laboratories are an essential work environment that plays an important role in diagnosing, treating, and being sensitive to diseases. One way to evaluate laboratories' performance is to calculate their efficiency. This study investigates the efficiency of laboratories that are related to health centers in the south of Iran. METHODS: This study was conducted in 2021. The input numbers include: the number of technical personnel and the number of cell counters, and the output data includes: the scores obtained from the level 2 health laboratory evaluation list. And efficiency was calculated with DEAP software. The analysis is accomplished by the assumption of input-oriented. FINDINGS: The efficiency of laboratories of Orzueeyeh and Ravar Cities had the highest efficiency with the assumption of variable returns to scale efficiency 1, and the model of all laboratories is the laboratory of Ravar City. The laboratories of Kuhbanan and Rabor cities had the lowest efficiency with the assumption of variable returns to scale efficiency of 0.859 and 0.899, respectively. The average scale efficiency, Variable returns to scale, and constant returns to scale for laboratories in the cities of Kerman province are 0.842, 0.943, and 0.895, respectively. CONCLUSIONS: To increase the efficiency of laboratories, significant resources and funds should be used, as well as few studies have been done on the efficiency of laboratories, which requires more attention.

Cost and efficiency analysis in Iranian primary health centers: a micro costing and data envelopment analysis.

BACKGROUND: Health Complex Model was implemented to provide primary health care services in urban, especially in slum areas. As a pilot at a provincial level, Chamran Health Complex offers healthcare for more than 57,000 residents of Tabriz. Despite the necessity of cost information in healthcare decision-making, there was limited knowledge about the unit cost of services. This study aims to analyze the cost and efficiency of health centers. METHODS: Activity-Based Costing method with direct and step-down allocation methods was adopted. We estimated unit costs in a hypothetical scenario according to national standards to quantify the gap between current and standard practice. Input-oriented Data Envelopment Analysis was administered to measure the efficiency of health centers. RESULTS: The total cost of the complex was $2,841,897, of which 67% ($1910373) and 33% ($931523) were accounted for direct and indirect costs, respectively. The vaccination center had the lowest ($9), and the occupational health center had the highest average unit cost ($76). The average technical efficiency of the health centers was 0.519, where the HC1 and HC3 showed the best performance. CONCLUSION: There is remarkable variability in service costs across health centers, which must be addressed in performance management and contracting practices. Although we found a gap between current and standard practice in terms of staff and facilities according to national standards, Chamran Health Complex has an untouched capacity that can be utilized with better planning and without incurring additional costs. It raises the need for revising national standards by the Iran Ministry of Health.

Integrating the water-energy-food nexus and LCA + DEA methodology for sustainable fisheries management: A case study of Cantabrian fishing fleets.

The fishing sector constitutes an important source of economic revenue in northern Spain. In this context, various research studies have focused on the application of the five-step Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodology to quantify environmental impacts of fishing systems. However, some of them have used environmental indicators that focus on individual environmental issues, hindering the goal of achieving integrated resource management. Therefore, in this study, the Water-Energy-Food (WEF) Nexus is employed as an integrative perspective that considers the synergies and trade-offs between carbon footprint, energy requirements, and water demand. The main objective of this study is to evaluate the operational efficiency and environmental impacts of Cantabrian fishing fleets. To this end, the combined use of LCA and DEA, along with the WEF Nexus, was applied to the Cantabrian purse seine fleet. DEA matrices were generated using the LCA-derived WEF nexus values as inputs to calculate efficiency scores for each vessel. Subsequently, based on the efficiency projections provided by the DEA model, a new impact assessment was performed to understand the eco-efficiency and potential environmental benefits of operating at higher levels of efficiency within this fleet. The average efficiency of the fleet was above 60 %. Inefficient units demonstrated a greater potential to reduce their environmental impacts (up to 65 %) by operating according to efficiency projections. Furthermore, the results revealed a strong dependence of environmental impacts on one of the operational inputs, i.e., fuel consumption. These findings highlight the significance of embracing holistic approaches that combine technical, economic, and social factors to achieve a sustainable balance in fisheries systems. In this regard, the five-step LCA + DEA method applied in conjunction with the WEF Nexus emerged as a suitable tool for measuring operational and environmental objectives.

Modelling efficiency in primary healthcare using the DEA methodology: an empirical analysis in a healthcare district.

BACKGROUND: Primary healthcare management efficiency conditions the functioning of specialized care and has a direct impact on the outcomes of the health system and its sustainability. The objective of this research is to develop models to evaluate the efficiency, including health outcomes, of the primary healthcare centres (PHC) of the Clínico - La Malvarrosa Health District in Valencia. METHODS: To evaluate efficiency, Data Envelopment Analysis (DEA) was used with output orientation and variable returns to scale, with panel data from the years 2015 to 2019. In rates per 10,000 inhabitants, the inputs are: medical and nursing staff and pharmacy cost. The outputs are: number of consultations, hospital emergencies, referrals, avoidable hospitalisations, avoidable mortality and pharmaceutical prescription efficiency. As exogenous variables: the percentage of population over 65 years old, over 80 and case-mix. Three models were developed, all of them with the same inputs and different combinations of outputs related to: healthcare activity, outcomes, and both, in order to study the influence of the different approaches on efficiency. Each model is analysed both without exogenous variables and with each of them. RESULTS: The efficiency results vary depending on the model used, although certain PHCs are always on, or very close to, the efficient frontier, while others are always inefficient. When healthcare activity outputs are considered, efficiency scores improve and the number of efficient PHCs increases. However, in general, the PHC score decreases throughout the evaluated period. This decrease is more pronounced when only activity outputs are included. CONCLUSIONS: DEA allows the inefficiencies of PHCs to be analysed and the efficient ones are clearly distinguished from the inefficient, although different efficiency scores are obtained depending on the model used. Evaluation can be according to healthcare activity, health outcomes or both, making it necessary to identify the expected objectives of the PHCs, as the perspective of the analysis influences the results.

Technical efficiency and its determinants in healthcare systems of underdeveloped countries.

This study aims to evaluate the technical efficiency levels of healthcare systems in underdeveloped countries and to reveal the impact of COVID-19 along with some other factors on the efficiency levels via a two-stage data envelopment analysis. The study covers panel data from 2013 to 2020. The results show that technical efficiency scores decreased during the pandemic period. It is also understood from the results that immunization and the ratio of population ages 65 and above have significant and negative effects on inefficiency scores while carbon dioxide emissions have significant and positive effects. It is thought that the results of this study can guide decision-makers regarding efficiency gains in healthcare systems.

Decoding worldwide efficiency: Linking government spending, corruption, gender inequality, and renewable energy practices.

Countries have become increasingly concerned about the impact of their activities and their alignment with sustainable development goals. Consequently, it is necessary to examine their performance efficiency in a unified manner, accounting for economic, environmental, and social variables. A country's performance efficiency is defined as the ratio of observed output to frontier output, given the country's productive resources. In this paper, we use the frontier technique of Data envelopment analysis (DEA) to estimate the frontier output. The objective of this study is to assess the performance efficiency of 111 worldwide countries and to examine whether gender inequality, the share of renewable energy consumption, the government control of corruption and government expenditure on education could explain differences in performance. We employed the non-parametric DEA model using three inputs (capital, labor, and primary energy consumption) and one output (GDP). We also considered an additional output (CO2 emissions); however, the results were identical to the one output model because CO2 emissions and primary energy consumption are highly correlated (0.96). We find that there is a negative impact of gender inequality, a positive impact of the share of renewable energy consumption and government control of corruption on performance efficiency. Our results also indicate that reducing CO2 emissions required transitioning towards renewable energy and using the energy efficiently. However, we did not find a positive relationship between government expenditures on education and country efficiency.

Assessing the logistics industry efficiency with a modified range adjusted measure.

How to accurately assess logistics industry efficiency to identify production issues and provide support for optimizing logistics efficiency has become a current research challenge. Range adjust measure (RAM) is a method for efficiency assessment in data envelopment analysis. Currently, the RAM model only applies under the variable returns to scale condition and not to other conditions. This paper aims to establish a modified RAM (MRAM) model by revising the ranges for inputs and outputs of the RAM model. Under the constant returns to scale (CRS) condition, we first develop the RAM-CRS model. Then, by introducing radial models to define the lower bounds of inputs and the upper bounds of outputs, the MRAM is proposed. The logistics data from 18 provinces are collected to validate the practicality of the MRAM model. We compare the results of the MRAM with those of the RAM-CRS and conclude that the range bounds under RAM-CRS are too tight, which results in efficiency values at a relatively low level. The MRAM with modified bounds appropriately alleviates this restriction. We also compare the MRAM and the additive model. The results show that the efficiency of the MRAM is more accurate.

Efficiency evaluation and promoter identification of primary health care system in China: an enhanced DEA-Tobit approach.

BACKGROUND: With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. METHODS: Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China's healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. RESULTS: The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. CONCLUSIONS: Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

Economies of scope in the Norwegian public hospital sector.

This study investigates the potential economies of scope in the Norwegian public hospital sector after a major structural and organizational reform. Economies of scope refers to potential cost savings occurring from the scope of production rather than the scale. We use a data driven approach to distinguish between relatively specialized and differentiated hospitals. Using registry data spanning the period 2013-2019, we use non-parametric data envelopment analysis with bootstrapping procedures to investigate the potential presence of economies of scope. This is done separately for three different dimensions of which hospital production can be either specialized or differentiated. The findings suggest that economies of scope are present in the Norwegian hospital sector, meaning that there are cost savings related to the optimal differentiation of the activity. It is difficult to conclude on how these findings relate to the reform.

Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression.

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.

Progressive optimization allocation model for carbon emission quotas: An empirical study of Jiangsu cities.

The rational allocation of carbon emission quotas is crucial for improving the orderly operation of carbon markets. As a major energy-consuming province in China, Jiangsu's cities must allocate allowable carbon emissions effectively to meet China's 2030 peaking target. This study aims to achieve an optimal allocation of carbon emission quotas by considering principles such as responsibility, efficiency, and equity. To accomplish this goal, we developed a three-stage progressive optimization allocation process. This process incorporates the entropy weight TOPSIS allocation model, zero-sum game-DEA model, and Gini coefficient. The initial allocation scheme revealed that cities in southern Jiangsu, which have higher economic development levels, also received higher carbon emission quotas, compromising efficiency and equity. In response, the second stage involves adjusting the allocation to optimize efficiency for each city, aligning the quotas more closely with historical levels. Finally, the third stage uses the Gini coefficient to further refine the allocation scheme, achieving a more balanced distribution that aligns responsibility, efficiency, and fairness. This research highlights the importance of a structured approach to carbon quota allocation, ensuring a balance that supports both environmental goals and equitable development.