Uncertainty, depreciation and industry growth

Samaniego, Roberto M.; Sun, Juliana Yu

doi:10.1016/j.euroecorev.2019.103314

Cited by 9 publications

(13 citation statements)

References 41 publications

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“…Several studies associate operating in an uncertain environment with the method of depreciation (Berg et al, 2001;Berg & Moore, 1989;Femminis, 2008). By all means, in an environment of uncertainty, there is a consensus that there is an aggregate economic activity (Samaniego & Sun, 2019). Accordingly, it seems that uncertainty significantly affects investment decisions, especially in high-depreciation industries, and it is covered in the literature about the depreciation method.…”

Section: Literaturementioning

confidence: 99%

“…More specifically, it proposes a model for evaluating investments not only in times when cash flows are uncertain but also in times when depreciation is uncertain. As mentioned before, several studies are using fuzzy numbers for cash flow (Berg et al, 2001;Berg & Moore, 1989;Khalili et al, 2014;Samaniego & Sun, 2019) but in these studies, the depreciation amount is fixed or predictable. However, this is unrealistic for periods when cash flow is completely interrupted (such as .…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned before, the constant depreciation amount significantly affects firms ' capital investment decisions in times when the number of future cash flows is uncertain (Jackson, 2008;Jackson et al, 2009;Ohrn, 2019). Managers take investment decisions when there is a large imbalance between productivity and capital stock, especially where the investment is irreversible (Samaniego & Sun, 2019). On the other hand, using a model in which even the depreciation amount itself is a fuzzy number under different scenarios, management can make more accurate investment decisions under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Depreciation Approach in an Uncertain Environment: Interval Type-2 Fuzzy Sets in Maritime

Akan

Kiracı

2021

Preprint

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This study aims to propose a novel model for the determination of depreciation in an environment of uncertainty. In the study, amortization methods were revised through Interval Type-2 Fuzzy (IT2FSs), and a new approach was proposed for investors to make decisions in an environment of cash flow is uncertainty. In the study, we provide options among different depreciation alternatives in the future investment decisions of maritime companies through the revision of the straight-line depreciation method and the double-declining balance depreciation method. The fuzzy depreciation alternatives we suggest in our study are not only suitable for maritime companies, but also companies in different industries.

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Section: Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Depreciation Approach in an Uncertain Environment: Interval Type-2 Fuzzy Sets in Maritime

Akan

Kiracı

2021

Preprint

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“…In view of how to calculate the cost and carbon emission in the transportation process, the relevant literatures showed that the asset investment [9,41], equipment depreciation [42][43][44], goods loss [11,12], energy consumption [45], insurance [46], labor [47], and operation management [48] in the logistics operation process should be considered. It should be emphasized that transportation distance, transportation time, and loading capacity are the most critical factors affecting cost and carbon emissions [8,49,50].…”

Section: The Identification Of Key Factorsmentioning

confidence: 99%

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

et al. 2020

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Energy conservation, cost, and emission reduction are the research topics of most concern today. The aim of this paper is to reduce the cost and carbon emissions and improve the sustainable development of sheep transportation. Under the typical case of the “farmers–middlemen–slaughterhouses” (FMS) supply model, this paper comprehensively analyzed the factors, sources, and types of cost and carbon emissions in the process of sheep transportation, and a quantitative evaluation model was established. The genetic algorithm (GA) was proposed to search for the optimal path of sheep transportation, and then the model solving algorithm was designed based on the basic GA. The results of path optimization indicated that the optimal solution can be obtained effectively when the range of basic parameters of GA was set reasonably. The optimal solution is the optimal path and the shortest distance under the supply mode of FMS, and the route distance of the optimal path is 245.6 km less than that of random path. From the cost distribution, the fuel power cost of the vehicle, labor cost in transportation, and consumables cost account for a large proportion, while the operation and management cost of the vehicle and depreciation cost of the tires account for a small proportion. The total cost of the optimal path is 26.5% lower than that of the random path, and the total carbon emissions are 36.3% lower than that of random path. Path optimization can thus significantly reduce the cost of different types and significantly reduce the proportion of vehicle fuel power cost and consumables cost, but the degree of cost reduction of different types is different. The result of the optimal path is the key to be explored in this study, and it can be used as the best reference for sheep transportation. The quantitative evaluation model established in this paper can systematically measure the cost and carbon emissions generated in the sheep transportation, which can provide theoretical support for practical application.

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“…The problem of attracting investment in basic industries to increase the pace of development is traditionally one of the most relevant and widely discussed in the world economic literature (see, for example, the works of Deli (2016), Samaniego and Sun (2019), de Rassenfosse and Jaffe (2018), and other authors). The peculiarities of the current state of the economies of many large countries, associated with the actions of a number of negative global factors, such as a decrease in economic activity as a result of the viral pandemic, sanctions and other non-market restrictions, and increasing strategic uncertainties, have increased the importance of searching for sources of funds necessary to restore and increase economic growth.…”

Section: Introductionmentioning

confidence: 99%