A review of quality of life (QOL) assessments and indicators: Towards a “QOL-Climate” assessment framework

Estoque, Ronald C.; Togawa, Takuya; Ooba, Makoto; Gomi, Kei; Nakamura, Shōgo; Hijioka, Yasuaki; Kameyama, Yasuko

doi:10.1007/s13280-018-1090-3

Cited by 78 publications

(58 citation statements)

References 93 publications

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“…In fact, urban greening alone-improvement of the provision, quality, and accessibility of urban green spaces-has been an important issue in the fields of environmental health, urban quality of life, urban ecology, and ecosystem-based adaptation in cities [60][61][62] . This is because urban green spaces provide valuable ecosystem services, including the regulation of micro-climates and urban temperatures and the purification of air 63,64 .…”

Section: Discussionmentioning

confidence: 99%

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

et al. 2020

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More than half of the world's population currently live in urban areas and are particularly at risk from the combined effects of the urban heat island phenomenon and heat increases due to climate change. Here, by using remotely sensed surface temperature data and socialecological indicators, focusing on the hot dry season, and applying the risk framework of the Intergovernmental Panel on Climate Change, we assessed the current heat health risk in 139 Philippine cities, which account for about 40% of the country's total population. The cities at high or very high risk are found in Metro Manila, where levels of heat hazard and exposure are high. The most vulnerable cities are, however, found mainly outside the national capital region, where sensitivity is higher and capacity to cope and adapt is lower. Cities with high levels of heat vulnerability and exposure must be prioritized for adaptation. Our results will contribute to risk profiling in the Philippines and to the understanding of city-level heat health risks in developing regions of the Asia-Pacific.

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Section: Discussionmentioning

confidence: 99%

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

et al. 2020

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“…Generally, a bibliometric analysis is performed to evaluate both research trends and scholarly networks in different research disciplines [28–32]. This type of analysis can also provide guidance to young and budding researchers [31].…”

Section: Introductionmentioning

confidence: 99%

“…This type of analysis can also provide guidance to young and budding researchers [31]. It can also encourage and challenge researchers to conduct further studies [32–34]. Other reviews have combined a bibliometric analysis with a systematic and/or conceptual-theoretical review (e.g., [18,32]).…”

Section: Introductionmentioning

confidence: 99%

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Bibliometric analysis of highly cited articles on ecosystem services

et al. 2019

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This paper presents global research trends involving highly cited articles on ecosystem services from 1981 to 2017 based on a bibliometric analysis of such articles from the SCI-E and SSCI databases of the Web of Science. The analysis revealed that there were 132 highly cited articles, most of which were published between 2005 and 2014. Based on author keywords, the term ecosystem services was strongly linked to biodiversity. The top three journals in terms of total number of highly cited articles published were Ecological Economics, PNAS, and Ecological Indicators. Despite ranking sixth overall, Science ranked first in both impact factor and total citations per article. The US, UK, Netherlands, Spain, and Sweden were the top five most productive and cooperative countries in the world based on total number of highly cited articles and co-authorship network, respectively. The US was highly connected to Canada, the Netherlands, China and the UK. Stockholm University and Stanford University were the most productive institutions in Europe and North America, respectively. Stanford University is associated with many scholars in the field of ecosystem services research because of the InVEST model. Robert Costanza was the most prolific and highly cited author, the latter being largely due to the first valuation of the world’s ecosystem services and natural capital, he and his co-authors published in 1997 in Nature. Terrestrial, urban, and forest ecosystems were the top types of ecosystems assessed. Regulating and provisioning services were the major ecosystem services studied. Quantitative and qualitative assessments were the main research focus. Most of these highly cited studies on ecosystem services are done on areas geographically located in North America and Europe.

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“…Recently there has been much debate surrounding the measuring of the quality of life of a society. Quality of life (QOL) reflects the relationship between individual desires and the subjective insight of accomplishments together with the chances offered to satisfy requirements [1]. It is associated with life satisfaction, well-being, and happiness in life.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring

et al. 2019

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For many years there has been a focus on individual welfare and societal advancement. In addition to the economic system, diverse experiences and the habitats of people are crucial factors that contribute to the well-being and progress of the nation. The predictor of quality of life called the Better Life Index (BLI) visualizes and compares key elements—environment, jobs, health, civic engagement, governance, education, access to services, housing, community, and income—that contribute to well-being in different countries. This paper presents a supervised machine-learning analytical model that predicts the life satisfaction score of any specific country based on these given parameters. This work is a stacked generalization based on a novel approach that combines different machine-learning approaches to generate a meta-machine-learning model that further aids in maximizing prediction accuracy. The work utilized an Organization for Economic Cooperation and Development (OECD) regional statistics dataset with four years of data, from 2014 to 2017. The novel model achieved a high root mean squared error (RMSE) value of 0.3 with 10-fold cross-validation on the balanced class data. Compared to base models, the ensemble model based on the stacked generalization framework was a significantly better predictor of the life satisfaction of a nation. It is clear from the results that the ensemble model presents more precise and consistent predictions in comparison to the base learners.

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A review of quality of life (QOL) assessments and indicators: Towards a “QOL-Climate” assessment framework

Cited by 78 publications

References 93 publications

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

Bibliometric analysis of highly cited articles on ecosystem services

Supervised Machine-Learning Predictive Analytics for National Quality of Life Scoring

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