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      Synergy of climate change with country success and city quality of life


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          Most people around the world have felt the effects of climate change on their quality of life. This study sought to achieve the maximum efficiency for climate change actions with the minimum negative impact on the well-being of countries and cities. The Climate Change and Country Success (C 3S) and Climate Change and Cities’ Quality of Life (C 3QL) models and maps of the world created as part of this research showed that as economic, social, political, cultural, and environmental metrics of countries and cities improve, so do their climate change indicators. For the 14 climate change indicators, the C 3S and C 3QL models indicated 68.8% average dispersion dimensions in the case of countries and 52.8% in the case of cities. Our research showed that increases in the success of 169 countries saw improvements in 9 climate change indicators out of the 12 considered. Improvements in country success indicators were accompanied by a 71% improvement in climate change metrics.

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          Safeguarding pollinators and their values to human well-being

          Wild and managed pollinators provide a wide range of benefits to society in terms of contributions to food security, farmer and beekeeper livelihoods, social and cultural values, as well as the maintenance of wider biodiversity and ecosystem stability. Pollinators face numerous threats, including changes in land-use and management intensity, climate change, pesticides and genetically modified crops, pollinator management and pathogens, and invasive alien species. There are well-documented declines in some wild and managed pollinators in several regions of the world. However, many effective policy and management responses can be implemented to safeguard pollinators and sustain pollination services.
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            Mapping synergies and trade-offs between energy and the Sustainable Development Goals

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              A review on Environmental Kuznets Curve hypothesis using bibliometric and meta-analysis

              The Environmental Kuznets Curve (EKC) hypothesis dates back in decades and is still topical presently due to its importance in environmental policy formulation. There are several systematic reviews of the EKC hypothesis using traditional review method. However, this review employs bibliometric and meta-analysis to track historical trends on the theme using the VOSviewer software and meta-analytic methods. The review translates the network analysis into visualized forms based on authors' contribution, the impact of the research by countries, citations count, and text corpus modeling using a network data extracted from Web of Science. The meta-analysis reveals that the collection of studies that validate the inversed-U shaped relationship has an average of US$8910 as the turning point of annual income level. Low income and middle-income countries are found below the thresholds of annual income level while high-income countries are above. Heterogeneity is confirmed among turning point in studies on EKC hypothesis due to differences in the period of study and econometric methods used in model estimation. The empirical findings reveal that most of the studies on EKC hypothesis are based on atmospheric indicators, while literature is sporadic and limited on EKC hypothesis which employs land indicators, oceans, seas, coasts and biodiversity indicators, and freshwater indicators.

                Author and article information

                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                15 May 2023
                15 May 2023
                : 13
                : 7872
                [1 ]GRID grid.9424.b, ISNI 0000 0004 1937 1776, Vilnius Gediminas Technical University, ; Vilnius, Lithuania
                [2 ]GRID grid.459524.b, ISNI 0000 0004 1769 7131, FLAME University, ; Pune, India
                [3 ]GRID grid.15751.37, ISNI 0000 0001 0719 6059, University of Huddersfield, ; Huddersfield, UK
                [4 ]GRID grid.6988.f, ISNI 0000000110107715, Tallinn University of Technology, ; Tallinn, Estonia
                [5 ]GRID grid.6441.7, ISNI 0000 0001 2243 2806, Vilnius University, ; Vilnius, Lithuania
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                : 13 February 2023
                : 12 May 2023
                Funded by: FundRef http://dx.doi.org/10.13039/501100010790, Erasmus+;
                Award ID: 2020-1-LT01-KA203-078100
                Award ID: 2020-1-UK01-KA226-HE-094662
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                © Springer Nature Limited 2023

                climate sciences,ecology,environmental sciences,natural hazards,planetary science,health care,energy science and technology,mathematics and computing,climate-change adaptation,climate-change impacts,climate-change mitigation,climate-change policy,energy and society,environmental economics,environmental impact,psychology and behaviour,socioeconomic scenarios,sustainability


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