• Energy Research
• 13. Climate action close
• 11. Sustainability close
• 8. Economic growth close
• RU close
• UA close

Aviation is one of the most significant growth drivers not only for the transportation infrastructure, but also for country’s economy as it contributes to both industrial and social progress of society. The paper focuses on Sheremetyevo International Airport, one of the largest Russian airports and analyzes it in terms of short-term and long-term perspectives. Using real statistical data, the article presents the models for forecasting essential performance indicators of airports, namely, passenger traffic and revenue. By comparing quality characteristics, most suitable models were selected and checked for adequacy. Forecasts of the probable changes in the values of the performance indicators were made under the assumption of maintaining the previously formed development trend.

Based on the children’s road traffic injuries analysis and field pedestrians’ behavior observations during the roadway crossing, in order to reduce accidents among pedestrians-children, to create safe approaches to educational institutions is proposed.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.INM.INM-CM5-H' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The INM-CM5-H climate model, released in 2016, includes the following components: aerosol: INM-AER1, atmos: INM-AM5-H (0.67x0.5; 540 x 360 longitude/latitude; 73 levels; top level sigma = 0.0002), land: INM-LND1, ocean: INM-OM5-H (North Pole shifted to 60N, 90E. 0.167x0.125; 2160x1440 longitude/latitude; 40 levels; vertical sigma coordinate), seaIce: INM-ICE1. The model was run by the Institute for Numerical Mathematics, Russian Academy of Science, Moscow 119991, Russia (INM) in native nominal resolutions: aerosol: 50 km, atmos: 50 km, land: 50 km, ocean: 10 km, seaIce: 10 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.INM.INM-CM4-8.ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The INM-CM4-8 climate model, released in 2016, includes the following components: aerosol: INM-AER1, atmos: INM-AM4-8 (2x1.5; 180 x 120 longitude/latitude; 21 levels; top level sigma = 0.01), land: INM-LND1, ocean: INM-OM5 (North Pole shifted to 60N, 90E; 360 x 318 longitude/latitude; 40 levels; sigma vertical coordinate), seaIce: INM-ICE1. The model was run by the Institute for Numerical Mathematics, Russian Academy of Science, Moscow 119991, Russia (INM) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Achieving economic development is one of the most important economic goals of every country. Identifying the determinants of economic growth, is a useful tool for adopting appropriate economic policies. This study, therefore, empirically examines the impact of trade openness, foreign direct investment, and financial development on economic growth, across 62 countries over the period 1995–2016. These countries are divided into two groups: low-income and high-income countries. We employ the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) estimation techniques on the cross-country panel data. The findings show a positive long run association between trade openness, foreign direct investment (FDI), financial development, labor, government expenditure, and economic growth in low-income countries, with a positive and negative short run effect from capital and government expenditures, respectively. For high-income countries, a positive long run association between trade openness, FDI, capital, and economic growth exist. The short run estimates indicate a positive effect on trade openness and capital as well as a negative effect on government expenditure. Our study shows that the adoption of policies that improves access to skilled labor and international trade, affect the attainment of a sustainable economic development.

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.

This research looked at the state of soils faced with urbanization processes in the Arctic region of the Yamal-Nenets Autonomous District (YANAO). Soils recently used in agriculture, which are now included in the infrastructure of the cities of Salekhard, Labytnangi, Kharsaim, and Aksarka in the form of various parks and public gardens were studied. Morphological, physico-chemical, and agrochemical studies of selected soils were conducted. Significant differences in fertility parameters between urbanized abandoned agricultural soils and mature soils of the region were revealed. The quality of soil resources was also evaluated in terms of their ecotoxicology condition, namely, the concentrations of trace metals in soils were determined and their current condition was assessed using calculations of various individual and complex soil quality indices.