Cost-efficiency assessments of marine monitoring methods lack rigor : a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis
Hyvärinen, H., Skyttä, A., Jernberg, S., Meissner, K., Kuosa, H., & Uusitalo, L. (2021). Cost-efficiency assessments of marine monitoring methods lack rigor : a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis. Environmental Monitoring and Assessment, 193(7), Article 400. https://doi.org/10.1007/s10661-021-09159-y
Julkaistu sarjassa
Environmental Monitoring and AssessmentTekijät
Päivämäärä
2021Tekijänoikeudet
© The Author(s) 2021
Global deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term “cost-efficiency.” We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost–benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost–benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.
...
Julkaisija
SpringerISSN Hae Julkaisufoorumista
0167-6369Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/97869932
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
Open access funding provided by Finnish Environment Institute (SYKE). This study is part of BONUS FUMARI project, which receives funds from BONUS (Art. 185). BONUS is jointly funded by the EU, the Academy of Finland, and the Swedish Research Council Formas.Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Improving statistical classification methods and ecological status assessment for river macroinvertebrates
Ärje, Johanna (University of Jyväskylä, 2016)Aquatic ecosystems are facing a growing number of human-induced stressors and the need to implement more biomonitoring to assess the ecological status of water bodies is eminent. This dissertation aims at providing tools ... -
Advanced performance monitoring for self-healing cellular mobile networks
Chernogorov, Fedor (University of Jyväskylä, 2015)This dissertation is devoted to development and validation of advanced per- formance monitoring system for existing and future cellular mobile networks. Knowledge mining techniques are employed for analysis of user specific ... -
Applying value of information and subsample selection to cost-efficient lake monitoring
Koski, Vilja (Jyväskylän yliopisto, 2024)Tässä väitöskirjassa hyödynnetään päätösanalyysin työkaluja ja osaotoksen valitsemista ympäristönsuojeluun liittyvässä päätöksenteossa. Käytännön tutkimuskysymys liittyy Suomen järvien hoitoon. Jos seuranta-aineistoon ... -
Systematic Literature Review on Cost-efficient Deep Learning
Klemetti, Antti; Raatikainen, Mikko; Myllyaho, Lalli; Mikkonen, Tommi; Nurminen, Jukka K. (Institute of Electrical and Electronics Engineers (IEEE), 2023)Cloud computing and deep learning, the recent big trends in the software industry, have enabled small companies to scale their business up rapidly. However, this growth is not without a cost – deep learning models are ... -
Monitoring in Biodiversity Offsetting
Moilanen, Atte; Jalkanen, Joel; Halme, Panu; Nieminen, Eini; Kotiaho, Janne S.; Kujala, Heini (Elsevier, 2024)Biodiversity offsetting is the process of using protection, habitat restoration and habitat maintenance to compensate for ecological damage to biodiversity caused by human activity, such as construction of infrastructure ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.