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Forests of the Americas and the Caribbean are undergoing rapid change as human populations increase and land use intensifies. We applied the IUCN Red List of Ecosystems (RLE) criteria and simple cost‐efficiency analyses to provide the first regional perspective on patterns of relative risk integrated across multiple threats. Based on six indicators of ecosystem distribution and function, we find that 80% of the forest types and 85% of the current forest area is potentially threatened based on RLE criteria. Twelve forest types are Critically Endangered due to past or projected future deforestation, and Tropical Dry Forests and Woodland have highest threat scores. To efficiently reduce risks to forest ecosystems at national levels, scenario analyses show that countries would need to combine large forest protection measures with focused actions, tailored to their sociopolitical context, to help restore ecological functions in a selection of threatened forest types.

Background As health expenditure continues to rise due to income growth, technological advancements, and an aging population, it has become increasingly important to accurately measure and improve the efficiency of health systems. This is because financial resources are limited, and the allocation of resources can significantly influence the quality of health systems and health outcomes. Methods This study applies machine learning techniques—Efficiency Analysis Trees (EAT) and Random Forest for Efficiency Analysis Trees (RFEAT)—to evaluate the efficiency of health systems in 36 OECD countries, comparing the results with those from the traditional free disposal hull (FDH) method. Results Analysis shows high discrimination power in the order of RFEAT, EAT, and FDH. The correlation in efficiency rankings shows more than 80% similarity between RFEAT and EAT, while both show less than 80% similarity with FDH. According to RFEAT estimates, the countries with the highest efficiency are South Korea, Switzerland, and Costa Rica, whereas the United States, Lithuania, and Latvia are identified as the least efficient. The group-level analysis reveals that Asian countries, on average, perform more efficiently followed by Oceania, Europe, and the Americas. The groups with higher out-of-pocket healthcare expenditures per capita tend to show slightly better efficiency and the group with the smallest elderly population proportion exhibits the highest average health system efficiency. Conclusion Traditional methods like FDH are prone to inefficiency underestimation, especially in small samples with multiple variables. This study demonstrates the potential of machine learning approaches like EAT and RFEAT to provide more reliable efficiency estimates. These methods can help policymakers make better resource allocation decisions by mitigating inefficiency underestimation and offering greater discrimination power.

This paper measures the efficiency of ports in the Yangtze River Delta Region （YRDR） in 2008 and 2013 using port berth quantity, quay length, and human resources as input indicators, using cargo and container throughput as output indicators, and considering traditional （foreign trade dependence and industrialization level） and modern environmental factors （traffic line density, financial development level, and informatization level）. To achieve such aim, this study constructs a multi-stage data envelopment analysis model （DEA） that identifies effective port decision-making units （DMUs） and generates a highly accurate conclusion by eliminating the inter- ference from the exogenous environment and random errors. First, the external environment significantly affects port efficiency, with the traditional environmental factors showing huge fluctuations and the modern environmental factors producing great benefits. Second, the efficiency of ports in YRDR has increased from 2008 to 2013 primarily because of their pure technical efficiency. Third, the weighted standard deviation ellipse （SDE） analysis results reveal that the efficiency pattern of ports significantly deviates from their throughput pattern, while the center of SDE of port efficiency moves from the eastern coastal regions to the northwest regions. Based on these find- ings, this paper proposes spatial development strategies for YRDR, such as creating an unblocked environment where spatial elements can freely circulate, intensifying port-city joint development, implementing differentiated policies, and focusing on the spatial collaboration of port efficiency.

1. Biodiversity assessments usually rely on indicators as surrogates for direct measures. Although the ecological validity of indicators has been extensively studied, their economic feasibility and cost‐effectiveness have seldom been assessed. 2. Here we present a novel generic framework for analysing the cost‐effectiveness of biodiversity indicators and the effect of budget allocations on the quality of biodiversity surveys. We sampled a suite of environmental and biological indicators in a Mediterranean ecosystem and calculated their cost‐effectiveness using measures of species richness, rarity and composition. 3. Environmental indicators were the cheapest indicator for richness and rarity but not for composition patterns, and they conveyed low accuracy (<70% of the variation in diversity patterns). For higher accuracy, plants and a combination of plants and insects provided the most cost‐effective indication of species richness, rarity and composition. Representation of composition patterns conveyed higher representation accuracy per given budget than richness patterns. 4. Marginal costs of improving the survey's ecological performance were high, making a taxonomically extensive sampling strategy non‐cost‐effective. Taxonomic identification of species‐rich invertebrate taxa is the major cost component in surveying these groups, and the availability of taxonomic expertise is a critical factor in determining their cost‐effectiveness. 5. We further illustrated the effects of socio‐economic context on the cost‐effectiveness of indicators by comparing the expected costs of conducting this survey in California and Morocco, two Mediterranean‐type regions at opposite socio‐economic extremes. Labour costs and the need for taxonomic out‐sourcing were the main sources of differences between regions, showing that cost‐effectiveness of indicators is, to a great extent, context‐dependent, and that the availability of in‐house taxonomic expertise is a major determinant. 6. Synthesis and applications. The acquisition of reliable data on biodiversity distribution is often a major limiting factor in effective conservation planning and management. We show that biodiversity representation and site prioritization can be conducted efficiently with limited funds by explicitly incorporating costs into the selection of indicators. The generic framework developed here for cost‐efficiency analysis of indicators can improve the quality and scope of biodiversity surveys and subsequently improve conservation decision‐making.

【Objective】Water shortage is a pressing issue in many regions of China, where significant differences exist in agricultural production conditions and efficiency among provinces, municipalities, and autonomous regions. Assessing the agricultural production efficiency of these regions is essential for identifying developmental bottlenecks and devising strategies to enhance both productivity and water use efficiency.【Method】A Data Envelopment Analysis (DEA) model was used to evaluate the comprehensive efficiency, pure technical efficiency, and scale efficiency of agricultural production in 31 provinces, municipalities, and autonomous regions across China from 2004 to 2022. Additionally, the DEA-Malmquist productivity index was applied to analyze the components of efficiency change and the temporal variation characteristics.【Result】The results indicate that, from 2004 to 2022, eight provinces and municipalities achieved DEA efficiency in agricultural production in all years, 12 regions were DEA efficient in some years, and 11 provinces and autonomous regions were consistently non-DEA efficient. Despite total factor productivity indices exceeding 1.000 both nationally and regionally, indicating an overall increase in agricultural production efficiency, the technical efficiency, pure technical efficiency, and scale efficiency remained largely unchanged. Technological progress was identified as the primary driver of the observed increase in efficiency. In regions that are non-DEA efficient, such as Hebei and Shanxi, redundant inputs in agricultural water resources and effective irrigated areas suggest that reducing excessive input or reallocating underutilized inputs could enhance production efficiency and the effectiveness of farmland water conservancy investments. Regions with fluctuating pure technical and scale efficiency indices around 1.000 should focus on optimizing production scale and improving the efficiency of existing technologies. Conversely, regions with low technological progress indices, such as Xinjiang and Yunnan, should prioritize technology optimization, adopt water-saving innovations, and enhance farmland water infrastructure.【Conclusion】Our findings reveal significant regional variations in agricultural production efficiency and total factor productivity across China. Strategies for improving these efficiencies are outlined, providing a basis for targeted interventions to enhance agricultural productivity and water use efficiency in water-scarce regions.

The present paper analyses both managerial and program efficiencies of Lithuanian family farms, in the tradition of Charnes et al. (Manag Sci 27 (6): 668–697, 1981) but with the important difference that multi-directional efficiency analysis rather than the traditional data envelopment analysis approach is used to estimate efficiency. This enables a consideration of input-specific efficiencies. The study shows clear differences between the efficiency scores on the different inputs as well as between the farm types of crop, livestock and mixed farms respectively. We furthermore find that crop farms have the highest program efficiency, but the lowest managerial efficiency and that the mixed farms have the lowest program efficiency (yet not the highest managerial efficiency).

Driven by the Ohrid Framework Agreement (OFA), decentralisation in Macedonia is a policy choice for spurring national cohesion rather than an economic instrument for the more efficient delivery of services. The OFA resulted in a new Macedonian Constitution with policies related to the decentralisation process and equitable representation of communities. This paper aims first to estimate the spending efficiency of Macedonian municipalities in service delivery from their own resources and, second, to ascertain the determinants of that efficiency. The ethnic fragmentation of municipalities is taken into account as a possible determinant of efficiency, which represents a novelty in such types of studies. We employ the DEA-VRS, Kernel and SFA estimation techniques to control for the robustness of our estimates. Our findings show that on average Macedonian municipalities exhibit low efficiency in service delivery, although there are significant variations among municipalities. The ethnic fragmentation of municipalities together with the population density, own tax revenues and political affiliation of the mayor with the leading political coalition at the state level explain differences in their efficiencies. More fragmented municipalities tend to be less efficient when providing services to citizens.

Background： Tertiary hospitals serve as the medical service center within the region and play an important role in the medical and health service system. They are also the key targets of public hospital reform in the new era in China. Through the reform of health system, the public hospital efficiency has changed remarkably. Therefore, this study aimed to provide some advice for efficiency assessment of public hospitals in China by comparing and analyzing the consistency of results obtained by three commonly used methods for examining hospital efficiency, that is, ratio analysis （RA）, stochastic frontier analysis （SFA）, and data envelopment analysis （DEA）. Methods： The theoretical basis, operational processes, and the application status of RA, SFA, and DEA were learned through literature analysis. Then, the empirical analysis was conducted based on measured data from 51 tertiary public hospitals in Beijing from 2009 to 2011. Results： The average values of hospital efficiency calculated by SFA with index screening and principal component analysis （PCA） results and those calculated by DEA with index screening results were relatively stable. The efficiency of specialized hospitals was higher than that of general hospitals and that of traditional Chinese medicine hospitals. The results obtained by SFA with index screening results and the results obtained by S FA with PCA results showed a relatively high correlation （r-value in 2009, 2010, and 2011 were 0.869, 0.753, and 0.842, respectively, P 〈 0.01）. The correlation between results obtained by DEA with index screening results and PCA results and results obtained by other methods showed statistical significance, but the correlation between results obtained by DEA with index screening results and PCA results was lower than that between results obtained by SFA with index screening results and PCA results. Conclusions： RA is not suitable for multi-index evaluation of hospital efficiency. In the given conditions, SFA is a stable efficiency analysis method. In the evaluation of hospital efficiency, DEA combined with PCA should be adopted with caution due to its poor stability.

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations.

To disentangle the sources of bank inefficiency, this paper presents an extended two-stage network multi-directional efficiency analysis (NMEA) approach by taking the internal structure of the banking system into account. The proposed two-stage NMEA approach extends the conventional “black-box” MEA approach, providing a unique efficiency decomposition and identifying which variables drive the inefficiency for banking systems with a two-stage network structure. An empirical application of Chinese listed banks from 2016 to 2020 during the 13th Five-year Plan reveals that the overall inefficiency of sample banks is primarily sourced from the deposit-generating subsystem. Additionally, different types of banks display differentiated evolution modes over different dimensions, confirming the importance of applying the proposed two-stage NMEA approach.