Indicators of air transport sustainable development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
World leaders gathered at the United Nations (UN) and adopted the 2030 Agenda for Sustainable Development. It is a plan of action aimed at achieving global sustainable development in economic, social and environmental areas, which ensures that no UN member state is left behind. The 17 sustainable development goals on the 2030 Agenda can be used as benchmarks for the coordinated development of UN member states. Aviation safety is an important component of the concept of general national security, the system of personal security, ecological and public safety and transport safety from external and internal threats. Maintaining an acceptable level of national aviation safety is a priority for the industry. The aviation transport is a part of the transport complex of Ukraine, which is an important component in the structure of the national economy and a link between all components of economic security to ensure the basic conditions of life and development of the state and society. The assessment of economic, technological, safety, social and ecological hazards is an integral part of all the logical blocks of the structural and functional scheme of strategic management of aviation safety in terms of sustainable development of the national economy. The task of the article is to determine and substantiate the main indicators of economic and technological development, safety, social and environmental components of air transport and assess their level. In the article the authors propose and present the dynamics in the period from 2010 to 2020 of 29 indicators of sustainable development of air transport of Ukraine, such us share of aviation transport in the gross value added (transport and communications); level of investment in aviation transport; level of export services of air transport; level of import services of aviation transport; level of shadowing of aviation transport; coefficient of manufacturability of aviation transport; capital utilization coefficient; level of shadow capital load; level of use of passenger capacity of aircraft and helicopters; level of renewal of fixed assets; cargo transport capacity of GDP by aviation transport; passenger transport capacity of GDP by aviation transport; average distance of cargo aviation transportation; average distance of passenger aviation transportation; ratio of domestic and international aviation transportation; catastrophes, accidents, serious coefficients for regular commercial/irregular commercial/non-commercial flights and execution of aviation works/training flights; level of wages in the production of aviation transport; level of employment in air transport; coefficient of population mobility; level of official GVA created by shadow wages; level of shadow employment; level of CO2 emissions of aviation transport of Ukraine to GDP; level of emissions of pollutants into the atmosphere; level of environmental costs of aviation transport. Authors determine their threshold and optimal values. Indicators are given in groups in the above areas. Indicators are divided into stimulants (indicators that contribute to the sustainable development of air transport and the national economy) and disincentives (indicators that hinder the sustainable development of air transport and the national economy). The solution of this problem will make it possible to conduct a comprehensive assessment of the current state of air transport in Ukraine on the basis of a systematic approach
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it