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Martin Feldstein's (1999) widely used taxable income formula for deadweight loss assumes the marginal social cost of evasion and avoidance equals the tax rate. This condition is likely to be violated in practice for two reasons. First, some of the costs of evasion and avoidance are transfers to other agents. Second, some individuals overestimate the costs of evasion and avoidance. In such situations, excess burden depends on a weighted average of the taxable income and total earned income elasticities, with the weight determined by the resource cost of sheltering income from taxation. This generalized formula implies the efficiency cost of taxing high income individuals is not necessarily large despite evidence that their reported incomes are highly sensitive to marginal tax rates.

Practitioners and academics hypothesize that when there is high diversity in resource consumption patterns, costing systems are more sensitive to errors. Given that firms' resources to enhance costing accuracy are typically constrained, it is argued that costing system refinement efforts should be focused on such cases, where they are likely to be most effective. However, little guidance is available on how to identify those situations where costing system refinement efforts (such as introducing an activity-based costing system) are likely to pay off most in terms of increased accuracy. Further, to our knowledge, the existing guidance provided by this high diversity rule of thumb has never been empirically tested. Using a simulation method, we address these issues in this paper. Specifically, we model various aspects, and degrees, of diversity in the resource consumption patterns to be reflected by the costing system and find that more diversity in resource consumption patterns only leads to increased costing system sensitivity to errors for some of the aspects of diversity studied. We also identify situations in which allocating costing system refinement resources to cases characterized by high diversity in resource consumption patterns is detrimental to improved accuracy.

Small- and medium-sized enterprises (SMEs) contribute enormously to a country’s sustainable growth. Developing the pathways that lead to sustainable innovation in SMEs represents an important aspect of the business world and society. The aim of this article is to verify the relations and pathways that lead to sustainable product innovation performance while considering all three pillars of the Triple Bottom Line Approach. This study used a mixed methods approach to identify the antecedents of sustainable product innovation performance. Our approach applied structural equation modeling and fuzzy-set qualitative comparative analysis. The structural equation model was used to measure the effects of the three pillars of the triple bottom line: economic, social, and environmental developments. The structural equation model was also designed to account for the firm’s type (Public Limited Companies vs. General Partnerships). Using the structural equation model, we determined whether a firm’s type moderates the effects of the three pillars. Furthermore, using fuzzy-set qualitative comparative analysis, we identified alternative configurations of conditions and determined those that are likely to lead to sustainable product innovation performance and those that result in its absence. The sample comprises data from 349 Portuguese small and medium enterprises. The findings show that social and environmental developments are two important antecedents for product innovation performance, and they contribute to different pathways that lead to product innovation performance. In addition, in General Partnerships, human resource costs are important for sustainable product innovation performance. Therefore, the results of both the quantitative and qualitative analyses underline the relevance of the triple bottom line approach to product innovation performance.

Article 9 of the Water Framework Directive (WFD) requires member states to take account not only of the principle of cost recovery of water services, including environmental and resource costs (ERCs), but also of the use of water pricing as an environmental policy instrument; nevertheless, no common methodology exists for the estimation of financial costs, nor is there a practical definition of ERC. The review of public evidence and scientific research regarding the effect of pricing on demand shows the limitations of water pricing and the need to integrate pricing and non-pricing instruments. Cost recovery remains a convenient policy for the financing of existing and future water infrastructures. This study offers a brief discussion on the theory and practice of pricing in Article 9 of the WFD and proposes the adoption of a more realistic approach to the implementation of cost recovery, one which abandons the unrealistic objective of monetisation of ERCs and proposes alternatives to the current emphasis on water pricing as a component of water resources management.

Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

Against the backdrop of accelerating restructuring in the global economy and trade landscape, understanding the evolutionary mechanisms of timber trade networks has become increasingly crucial. Utilizing cross-national timber trade data from 2000 to 2024, this study applies a Stochastic Actor-Oriented Model to analyze the dynamic evolution of the timber trade network by incorporating multidimensional factors, including trade costs, resource costs, network structure, and trade structure. The findings reveal that: (1) Endogenous network mechanisms—particularly the triadic closure effect—play a dominant role in the formation of trade relationships; (2) resource-rich countries exhibit an export expansion with import restriction phenomenon, actively expanding exports while restricting imports to safeguard resource sovereignty; (3) timber price alone insufficiently reshapes trade ties, whereas sustainable forest management significantly drives network dynamics; and (4) net exporters favor developed economies via market screening. Economic development asymmetrically moderates trade—boosting exports in net exporters while curbing imports in net importers. This study moves beyond traditional economic perspectives, uncovering the profound effects of structural embeddedness and strategic behavior in timber trade, and the findings extend the theoretical framework for resource-based product commerce and provide empirical foundations for formulating equitable and sustainable forestry trade policies.

Convolutional Neural Networks (CNNs) have achieved remarkable results in many application fields. However, these CNNs have a large number of network parameters, thereby consuming a lot of computation and storage resources. This makes CNNs unable to be effectively applied to these platforms with limited storage and computation resources. To address this issue, this paper proposes a new compact convolution module called DSC-Ghost-Conv, which combines the advantages of both depthwise separable convolution (DSC) and Ghost convolution module (Ghost-Conv). DSC-Ghost-Conv replaces the standard convolution used in the Ghost convolution module with depthwise separable convolution so as to reduce resource costs of the Ghost convolution module. DSC-Ghost-Conv can be used as a plug-and-play component to implement ordinary convolutional layers in typical CNNs such as VGG-16, ResNet-50 and GoogleNet. Experimental results on the MNIST and CIFAR-10 datasets show that implementing the ordinary convolutional layers of CNNs with DSC-Ghost-Conv not only obtains the competitive performance to typical CNNs, but also greatly reduces the number of network parameters and floating point operations (FLOPs) of CNNs. This demonstrates that the proposed DSC-Ghost-Conv can effectively reduce the resource costs of CNNs.

Understanding farm-level efficiencies of resource use is critical in comparisons of the sustainability of aquaculture production systems. We developed a set of practical resource-use efficiency metrics to calculate and compare resource-use efficiency with resource-cost efficiency across major species and production systems in US aquaculture. Results showed that no one production system used all resources most efficiently. Intensive pond production of channel catfish Ictalurus punctatus demonstrated the greatest efficiency in the use of water, energy, labor, management, and capital resources, while RAS production was most efficient in terms of land and feed use. Among the wide array of pond scenarios examined, more intensive scenarios generally were more efficient in terms of several metrics, but economic sustainability also depends upon business models that effectively meet differing demand requirements of customers. Thus, less intensive production systems were economically sustainable in areas with relatively abundant land and water resources available at lower cost. Labor efficiencies varied widely across scenarios analyzed. Given increasing concerns related to the availability of labor for aquaculture farming in the USA, greater attention to the efficiency of labor on farms is warranted. The metrics used were aligned with common farm management tools (e.g. enterprise budgets) that allow for ease of use by farms and researchers to assess effects on comparative resource-use efficiencies of new farming practices and technologies under development.

This paper evaluates the effectiveness of the 2009 French Diagnosis-Related Group (DRG) classification reform, which introduced four severity levels within each DRG, ranging from low to very high, with corresponding increases in fixed-price reimbursements. Notably, the reform incorporates the Medicare Severity Diagnosis-Related Group (MS-DRG) system, first implemented in the United States in 2007, giving the French system international relevance. The French Public Health Insurance system (NHI) reimburses both public and private healthcare establishments through a DRG-based payment system. This study focuses on variations in hospital resource costs for four different health conditions. The paper begins by discussing the theoretical challenges of constructing DRG categories, particularly the trade-off between greater clinical detail (granularity) and the risk of distorting incentives for hospital efficiency. It then presents an empirical analysis of hospital resource cost variations both within and between DRGs for the same pathology or clinically meaningful group (DRG-root), using data from 2012 to 2019. Our findings suggest that a one-size-fits-all approach to severity classification is inadequate. In some cases, broader categories improve statistical validity, while in others, more granular distinctions are necessary. We conclude that a tailored, case-by-case approach is the most effective solution. Specifically, the analysis reveals significant overlap in confidence intervals for hospital resource costs across DRG severity levels, suggesting that the current classification system fails to effectively capture cost differences related to severity. Additionally, a large portion of cost variation within DRGs is driven by factors unrelated to severity, such as hospital-specific characteristics. Overall, the results underscore the need to revise the current DRG system in France in order to reduce financial discrepancies and to prevent incentives for patient selection, especially before implementing bundled payment models that include both inpatient and outpatient care. Highlights • The 2009 French DRG reform adopted a four-level severity system with corresponding increases in fixed-price ranges, based on the 2007 U.S. Medicare Severity Diagnosis-Related Group (MS-DRG) model, giving the results international relevance; • Within a DRG, the confidence intervals for average hospital resource costs tend to overlap or merge, and the magnitude of these intervals varies randomly; • Based on patient severity level, this revised, refined French-DRG classification does not prevent patient selection or skimming;  • Variability in hospital costs within DRG depends on the hospital's ownership and the type of care (medical or surgical); • This DRG cost variability study provides crucial insights to provide guidance in the design or redesign of reimbursement schemes by policymakers.