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Sustainable Development

Sustainable Development: A critical concept that encompasses the responsible use of resources to meet current needs without compromising the ability of future generations to meet their own.
This multifaceted approach integrates economic, social, and environmeental considerations to foster long-term, equitable growth and environmental preservation.
Key aspects include renewable energy, resource conservation, waste reduction, and sustainable consumption patterns.
Promoting Sustainable Development is essential for addressing global challenges like climate change, poverty, and environmental degradation in a holistic manner.

Most cited protocols related to «Sustainable Development»

1
Molecular and Morphological Study of Labiobaetis
All specimens were collected between 1994 and 2020 by Dr. Hendrik Freitag and his team (Ateneo de Manila University) and preserved in 70%–96% ethanol.
The dissection of larvae was done in Cellosolve (2-Ethoxyethanol) with subsequent mounting on slides with Euparal liquid, using an Olympus SZX7 stereomicroscope.
The DNA of part of the specimens was extracted using non-destructive methods allowing subsequent morphological analysis (see Vuataz et al. 2011 (link) for details). We amplified a 658 bp fragment of the mitochondrial gene cytochrome oxidase subunit 1 (COI) using the primers LCO 1490 and HCO 2198 (Folmer et al. 1994 (link); see Kaltenbach and Gattolliat 2020 (link) for details). Sequencing was done with Sanger’s method (Sanger et al. 1977 (link)). The genetic variability between specimens was estimated using Kimura-2-parameter distances (K2P, Kimura 1980 (link)), calculated with the program MEGA 7 (Kumar et al. 2016 (link), http://www.megasoftware.net).
The GenBank accession numbers are given in Table 1, nomenclature of gene sequences follows Chakrabarty et al. (2013) (link).
The nomenclature used for Molecular Operational Taxonomic Units (MOTUs) is somewhat different as the one proposed by Morard et al. (2016) (link).
Drawings were made using an Olympus BX43 microscope. To facilitate the determination of species and the comparison of important structures, we partly used a combination of dorsal and ventral aspects in one drawing. Explanations are given in Fig. 1.
Photographs of larvae were taken using a Canon EOS 6D camera and the Visionary Digital Passport imaging system (http://www.duninc.com) and processed with Adobe Photoshop Lightroom (http://www.adobe.com) and Helicon Focus version 5.3 (http://www.heliconsoft.com). Photographs were subsequently enhanced with Adobe Photoshop Elements 13.
The distribution maps were generated with SimpleMappr (https://simplemappr.net, Shorthouse 2010 ). Google Earth (http://www.google.com/earth/download/ge/) was used to attribute approximate GPS coordinates to sample locations of Müller-Liebenau (1982) .
The taxonomic descriptions were generated with a DELTA (Dallwitz 1980 (link); Dallwitz et al. 1999 ; Coleman et al. 2010 (link)) database containing the morphological states of characters of the Labiobaetis species of the Philippines.
The dichotomous key was elaborated with the support of DKey version 1.3.0 (http://drawwing.org/dkey, Tofilski 2018 (link)).
The terminology follows Hubbard (1995) and Kluge (2004) (link). The character states of some of the characters are depicted in Fig. 2.
Abbreviations:
AdMU Ateneo de Manila University, Quezon City (Philippines)
MZL Musée de Zoologie Lausanne (Switzerland)
PCSD Palawan Council for Sustainable Development, Puerto Princesa, Palawan (Philippines)
PNMMuseum of Natural History of the Philippine National Museum, Manila (Philippines)
ZSMZoologische Staatssammlung München (Germany).
Kaltenbach T., Garces J.M, & Gattolliat J.L. (2020). The success story of Labiobaetis Novikova & Kluge in the Philippines (Ephemeroptera, Baetidae), with description of 18 new species. ZooKeys, 1002, 1-114.
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Publication 2020
2-ethoxyethanol 6-amino-5-(formyl-N-methylamino)-1,3-dimethyluracil Cellosolve Character Cytochromes a1 Dissection Ethanol Genes Genes, Mitochondrial Larva Microscopy Microtubule-Associated Proteins Oligonucleotide Primers Oxidases Protein Subunits Reproduction Sustainable Development
2
Incidence: A Robust Tool for Epidemic Curve Analysis
The philosophy underpinning the development of
incidence is to ‘do the basics well’. The objective of this package is to provide simple, user-friendly and robust tools for computing, manipulating, and plotting epidemic curves, with some additional facilities for basic models of incidence over time.
The general workflow (
Figure 1) revolves around a single type of object, formalised as the S3 class
incidence.
incidence objects are lists storing separately a matrix of case counts (with dates in rows and groups in columns), dates used as breaks, the time interval used, and an indication of whether incidence is cumulative or not (
Figure 1). The
incidence object is obtained by running the function
incidence() specifying two inputs: a vector of dates (representing onset of individual cases) and an interval specification. The dates can be any type of input representing dates including
Date and
POSIXct objects, as well as numeric and integer values. The dates are aggregated into counts based on the user-defined interval representing the number of days for each bin. The interval can also be defined as a text string of either "week", "month", "quarter", or "year" to represent intervals that can not be defined by a fixed number of days. For these higher-level intervals, an extra parameter—
standard—is available to specify if the interval should start at the standard beginning of the interval (e.g. weeks start on Monday and months start at the first of the month).
incidence() also accepts a
groups argument which can be used to obtain stratified incidence. The basic elements of the
incidence object can be obtained by the accessors
get_counts(),
get_dates(), and
get_interval().
This package facilitates the manipulation of
incidence objects by providing a set of handler functions for the most common tasks. The function
subset() can be used for isolating case data from a specific time window and/or groups, while the [ operator can be used for a finer control to subset dates and groups using integer, logical or character vectors. This is accomplished by using the same syntax as for matrix and data.frame objects, i.e.
x[i, j] where
x is the
incidence object, and
i and
j are subsets of dates and groups, respectively.
The function
pool() can be used to merge several groups into one, and the function
cumulate() will turn incidence data into cumulative incidence. To maximize interoperability,
incidence objects can also be exported to either a matrix using
get_counts() or a data.frame using
as.data.frame(), including an option for a ‘long’ format which is readily compatible with
ggplot2 (
Wickham, 2016 ) for further customization of graphics.
In line with RECON’s development guidelines, the
incidence package is thoroughly tested via automatic tests implemented using
testthat (
Wickham, 2011 ), with an overall coverage nearing 100% at all times. We use the continuous integration services
travis.ci and
appveyor to ensure that new versions of the code maintain all existing functionalities and give expected results on known datasets, including matching reference graphics tested using the visual regression testing implemented in
vdiffr (
Henry
et al., 2018). Overall, these practices aim to maximise the reliability of the package, and its sustainable development and maintenance over time.
Kamvar Z.N., Cai J., Pulliam J.R., Schumacher J, & Jombart T. (2019). Epidemic curves made easy using the R package incidence. F1000Research, 8, 139.
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Publication 2019
Character Cloning Vectors Epidemics Reading Frames Sustainable Development
3
Citizen Science: Diverse Meanings and Practices
Perhaps the most elusive problem in describing CS originates from the multiple meanings of the concept itself. On a qualitative level this is evident by observing how two distinct meanings have developed in the natural- and social sciences respectively since the early to mid-1990s.
The most common conception of the meaning of CS, which in recent years has gained significant momentum in the natural sciences, originates in the type of research described by Bonney et al. who attest that “[i]n the past two decades, CLO’s [Cornell Laboratory of Ornithology] projects have engaged thousands of individuals in collecting and submitting data on bird observations” (p. 977). This practice, however, goes back at least to the 1960s and is sometimes even extended to include the National Audubon Society’s annual Christmas Bird Count, beginning in the year 1900, even if the name “citizen science” was not used until the 1990s [4 ]. In this line of research, the focal point for volunteer contributions consists of participation in observations, classification and collection of data, which in turn are used by scientists. There are important synonyms to the concept of CS in this case, including ‘community-based monitoring’ [3 (link)], ‘volunteer monitoring’ [11 (link)] and ‘participatory science’ [12 (link)], all designating the contribution of non-scientists to (primarily natural-) science.
On the other hand we find a very influential notion originating in the social sciences, as expressed in the account of Irwin’s 1995 book “Citizen Science: A Study of People, Expertise and Sustainable Development” where CS is defined as “/…/ a science which assists the needs and concerns of citizens /…/ a form of science developed and enacted by citizens themselves”[13 ]. This vision and approach has been widely adopted in the social sciences and by policy-makers, but it also describes and envisions research on health, such as in “popular epidemiology” [14 (link)]. In practice this conception of CS is understood largely as the roles of citizens as stakeholders in processes of scientifically informed decision-making.
These two major understandings do not, however, exhaust all forms of CS that are of relevance for researchers interested in this phenomenon. There is also a plethora of concepts that have been coined to describe primarily local and activist-oriented forms of CS. These are more difficult to trace via scientometric methods because the results are not published in peer-reviewed literature. Instead the data from these studies are mainly used for direct interventions in policy-making and litigations. However, such interventions are often made visible by social scientists doing research on the phenomenon of CS. For example, there are cases of activist-oriented CS were data are scientifically validated and used for legal action against polluting industries [15 (link),16 (link)], Geographical Information Systems research for promoting the rights of indigenous peoples [17 (link)] and ‘civic technoscience’ for developing affordable instruments that can be used for monitoring oil spills and green urban areas [18 (link)]. Here we also find terms such as ‘community based auditing’ [19 (link)], ‘civic science’ [20 (link)] and ‘community environmental policing’ [16 (link)], ‘street science’ [21 ] and ‘popular epidemiology’ [14 (link),22 (link),23 (link)]. Finally, there are examples of organizational studies of ‘crowd science’ [24 (link)] and policy documents describing CS as ‘Do It Yourself Science’ [25 ]. These, however, only make sporadic appearances in the scientific literature.
Kullenberg C, & Kasperowski D. (2016). What Is Citizen Science? – A Scientometric Meta-Analysis. PLoS ONE, 11(1), e0147152.
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Publication 2016
Aves Conception Litigation Petroleum Pollution Policy Makers Sustainable Development Vision Voluntary Workers
4
Developing a Sustainable Healthcare Quality Framework
In the research, it was necessary in the first stage to establish the areas that make up the new reference framework.
The fields of the new reference framework were established by exploring the medical scientific literature and by collecting the most relevant ideas, which allow a causal and significantly positive relationship between a good organization and sustainability [14 (link)].
At this stage, in view of Isaksson’s conclusions [15 (link)], it was considered the symbiosis of practical interest between quality management and sustainability, which need to be further explored for a healthcare unit. Zdravkovic and Radukic [16 ] point out that, in addition to the 3 established areas of sustainability, social, environmental and economic, it is advisable to incorporate the institutional dimension which can manage sustainable development, in the interest of present and future generations.
After analyzing the 4 conceptual components of the frameworks developed so far and presented in the literature, we considered medical services as the 5th core area of a healthcare facility.
The conceptual model for the development of the San-Q framework is presented in Figure 1, which incorporates the legislation and medical standards, as well as the indicators from the existing reference frameworks [17 (link)].
Based on the global requirements, standards, procedure and methodology for the evaluation and accreditation of hospitals [18 ], as well as the standards of the National Authority for Quality Management in Health (ANMCS) for outpatient health services [19 ], but also regional specificity, the San-Q framework has been developed. It takes into account that the healthcare system can and should play a key role in promoting community coherence and should also seek environmental responsibility and financial stability.
For this reason, the San-Q reference framework is structured on the 3 dimensions of sustainable development, social, environmental and economic, to which the quality of institutional governance and healthcare are added, that are integrated in the seven core subjects of the standard ISO26000—Social responsibility guidelines [20 ], governance, human right, labor, environment, business practices, consumer and community, adapted to the context of healthcare provision (Figure 2).
With this support, the San-Q reference framework was structured on the 4 main phases of the quality cycle, Planning—Implementation—Evaluation—Review (PIER), that were adapted to the healthcare design of medical services provision, medical services provision, medical services evaluation and continuous improvement.
In the Planning phase, the San-Q reference framework includes the design of medical services provision that covers all aspects related to the definition of healthcare provision in the healthcare facilities. The Implementation phase consists of providing healthcare services that represent the process of treating patients. In the Evaluation phase, the San-Q reference framework includes the evaluation of healthcare services that comprises all aspects related to the evaluation of patient satisfaction, the effectiveness of treatment and the evaluation of medical staff satisfaction. The Review phase takes place after the provision of healthcare services and their evaluation, by conducting self-assessments and redesigning healthcare services, thus ensuring continuous improvement.
In order to establish the indicators that make up the San-Q reference framework, each of the four phases of the quality cycle were divided into two basic activities: P.A. Accreditation of healthcare services, P.B. Patient-centered care interventions design, I.A. Healthcare provision, I.B. Transfer assurance, E.A. Local opinion leaders’ evaluation and involvement, E.B. Satisfaction assessment, R.A. Self-assessment and R.B. Healthcare services innovation.
The core activities of the continuous improvement cycle for the sustainable development of the healthcare facility are represented in Figure 3.
Moldovan F., Blaga P., Moldovan L, & Bataga T. (2022). An Innovative Framework for Sustainable Development in Healthcare: The Human Rights Assessment. International Journal of Environmental Research and Public Health, 19(4), 2222.
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Publication 2022
Concept Formation Health Services, Outpatient Medical Staff Obstetric Labor Patient-Centered Care Patients Satisfaction Self-Assessment Sustainable Development Symbiosis
5
Measuring Ecological Wellbeing Performance
Figure 2 shows the measurement index system for studying the EWP of China’s provinces. The left input side is divided into two parts: natural resource inputs and non-natural resource inputs. Zhang et al. (2018) define EWP as the efficiency of the process of transforming ecological consumption into human well-being [30 (link)]. The human processing of resources into natural products that enhance human well-being requires technological, capital, and labor inputs as intermediate means [36 (link)], which are indispensable for the transformation from ecological consumption to human well-being. Income, education, and health cover economic and social dimensions, when extending EWP to the context of sustainable development, the whole process must be compatible with the environment. Reinhard et al. (2000) put environmental pollution as a cost in the production process on the input side when calculating environmental efficiency [48 (link)]. At the same time, this study argues that, in converting ecological consumption into well-being level, environmental pollution is a factor that reduces the comprehensive human well-being level. Its resource-like consumption that leads to environmental pollution has been put as a cost on the input side, and putting environmental pollution as an input again leads to double counting. Thus, this study corrects EWP by making environmental pollution-type indicators as undesired outputs. EWP is defined as the efficiency of transforming natural and non-natural resource consumption to a comprehensive well-being level that includes economic, social, and environmental well-being, corrected by environmental pollution.
Wang S., Zhang Y, & Yao X. (2021). Research on Spatial Unbalance and Influencing Factors of Ecological Well-Being Performance in China. International Journal of Environmental Research and Public Health, 18(17), 9299.
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Publication 2021
Environmental Pollution Homo sapiens Manpower Natural Products Obstetric Labor Sustainable Development

Most recents protocols related to «Sustainable Development»

1
Scoping Review on Older Adults and SDGs
In keeping with the Cochrane guidelines, we chose a scoping review approach because the literature has not previously been comprehensively reviewed and is heterogeneous in nature, consisting of peer-reviewed literature, international and national policy documents, and policy appraisals [16 ]. Searches were conducted between April and May 2021; peer-reviewed literature searches (in Scopus, Medline, and Global Health databases) were followed by grey literature searches (in Google and on the websites of the UN, WHO, Centre for Ageing Better, International Federation of Ageing, and Organisation for Economic Co-operation and Development). The searches were limited to a four year period, running from the introduction of the SDGs (in January 2016 [11 ]) to the start of the Decade of Healthy Ageing (in January 2021 [17 ]). Grey literature searches were limited to the first 100 results. Table 1 depicts the search strategy; the searches were adapted for each database to be reproducible.

Search strategy for the scoping review

ThemesSearch Terms
Theme 1, Population:(*aged OR retir* OR *age OR ageing OR *aging OR *old OR elder* OR senior* OR pension*) AND
Theme 2, Concept:(SDG* OR “sustainable development goal*”) AND
Theme 3, Context:communit* OR city or cities or town* OR village* or neighbourhood* OR residence
Limits:English Language; published between 2016 to 2020; humans

Notes: The timeline was chosen due to the introduction of the Sustainable Development Goals in January 2016 [11 ] and launch of United Nations Decade of Healthy Ageing in 2021 [17 ]

Abbreviations: SDG = Sustainable Development Goal

First, titles were screened by VS; then, abstracts and full texts were double screened by VS and CM, with discrepancies resolved via discussion. After screening and inclusion of papers, reference lists of the peer-reviewed articles were searched to avoid any data being omitted; also, forward searches were conducted to identify relevant papers that referenced the peer-reviewed articles.
Extraction of data from each document or study was streamlined using adaptations of existing frameworks [18 ]. We summarised the results narratively and descriptively to align with the objectives of the review. Quality assessment is not a standard procedure for scoping reviews [16 ] and was not conducted. However, a SPIDER search tool, which determined the inclusion and exclusion criteria for the review, was used (provided in Supplementary Table 1). The document selection process is shown in Fig. 1.

Flow diagram of the study selection strategy

Shevelkova V., Mattocks C, & Lafortune L. (2023). Efforts to address the Sustainable Development Goals in older populations: a scoping review. BMC Public Health, 23, 456.
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Publication 2023
Acclimatization elder flower Genetic Heterogeneity Spiders Sustainable Development TimeLine
2
Spatial Modeling for Agroforestry Prioritization

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Publication 2023
Anthropogenic Effects Ecosystem Forests Regeneration Sustainable Development
3
Preparation of Dicranopteris Leaf Powder
The fresh and matured leaves of D. indica were collected from Imphal West, Manipur (N24°49.258’, E093°56.411’) and authenticated by Dr. Biseshwori Thongam, Scientist-E (Taxonomist), Institute of Bioresources and Sustainable Development, Imphal, Manipur, with voucher number IBSD/M-284. They were properly washed and semi-dried in shade for 4-5 days. The samples were finely powdered using an electric grinder and packed in air-tight poly bags for further use.
Singh K.D., Koijam A.S., Bharali R, & Rajashekar Y. (2023). Insecticidal and biochemical effects of Dillenia indica L. leaves against three major stored grain insect pests. Frontiers in Plant Science, 14, 1135946.
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Publication 2023
Electricity Poly A Sustainable Development
4
Evaluating Global Sustainability Indicators
The study used data from the SDG Index, Global Innovation Index (GII), and World Bank. Data on 122 countries were gathered for the years 2015 and 2020. The year 2015 was selected because it was when the 17 SDGs were adopted, while 2020 was the latest year with available data. The World Bank offers a data analysis and visualization tool for various topics such as health, corruption, economic growth, and poverty. The tool uses time-series data. It is flexible, allowing the creation of tables and graphs that are easy to share and save (World Bank, 2021 ). The GDP per capita values (economic facilitator) were gathered from the World Bank database. The GDP per capita data for the year 2019 were used because those for the year 2020 were not yet available at the time of the study.
The SDG Index and Dashboards provide a set of indicators for monitoring attainment of the SDGs and for complementing the standardization and compilation by national and international organizations (SDG Index, 2021 ). The data on the SDG Index (sustainable development) were collected from the SDG Index and Dashboards database. In the case of this variable, data for 2016 were collected because data for 2015 were not available. This data unavailability is one of the limitations of the study.
The GII captures the characteristics and trends of the global and national innovation ecosystems through new approaches and metrics (WIPO, 2021 ). This index provides data that enable both the assessment of innovative performance and the introduction of new policy measures of innovation. This index provides data on the institutional framework, human capital and research, and ecological sustainability (institutional, knowledge, and environmental facilitators, respectively).
Chaparro-Banegas N., Ibañez Escribano A.M., Mas-Tur A, & Roig-Tierno N. (2023). Innovation facilitators and sustainable development: a country comparative approach. Environment, Development and Sustainability.
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Publication 2023
Ecosystem Head Homo sapiens Sustainable Development
5
Modeling Sustainable Development Drivers
Multiple linear regression (MLR) enables the modeling and examination of a linear relationship between explanatory variables and an explained variable (Field et al., 2012 ). The aim of the MLR analysis in this study was to identify how accurately the selected independent variables (economic, institutional, knowledge, and environmental facilitators) explain the dependent variable (sustainable development) for the years 2015 and 2020. Stepwise linear regression was used to select or eliminate independent variables because all of these independent variables were considered to have an equal probability of explaining sustainable development. Sustainable development was represented by an indicator (SDG Index) that determines the extent to which countries achieve the SDGs (Sachs et al., 2020 ). Stepwise linear regression highlighted the variables that provided the model that best fit the data without introducing researcher bias.
Chaparro-Banegas N., Ibañez Escribano A.M., Mas-Tur A, & Roig-Tierno N. (2023). Innovation facilitators and sustainable development: a country comparative approach. Environment, Development and Sustainability.
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Publication 2023
Sustainable Development

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Streptavidin phycoerythrin by Thermo Fisher Scientific
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Streptavidin-phycoerythrin is a fluorescent conjugate used for detection and quantification in various bioanalytical applications. It consists of the protein streptavidin covalently attached to the fluorescent dye phycoerythrin. The high-affinity binding between streptavidin and biotin allows for sensitive detection of biotinylated molecules.
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More about "Sustainable Development"

Sustainable development encompasses the responsible use of resources to meet current needs without compromising the ability of future generations to meet their own.
This multifaceted approach integrates economic, social, and environmental considerations to foster long-term, equitable growth and environmental preservation.
Key aspects include renewable energy, resource conservation, waste reduction, and sustainable consumption patterns.
Promoting sustainable development is essential for addressing global challenges like climate change, poverty, and environmental degradation in a holistic manner.
Discover how PubCompare.ai, an AI-driven platform, revolutionizes sustainable research by optimizing protocols and identifying the most effective solutions from literature, pre-prints, and patents.
Streamline your research and drive innovation in sustainability with this cutting-edge tool.
Synonyms and related terms include sustainable living, eco-friendly practices, green initiatives, and environmental stewardship.
Abbreviations like SDGs (Sustainable Development Goals) and ESG (Environmental, Social, and Governance) are also relevant.
Subtopics encompass renewable energy sources (e.g., solar, wind, hydroelectric), circular economy, sustainable agriculture, green urban planning, and more.
Enrich your understanding with insights from related technologies like Streptavidin-phycoerythrin, a versatile fluorescent label used in bioassays, and the LSE™ digital microplate shaker, which facilitates efficient sample preparation for sustainable research.
Explore the potential of tools like UP-213, a high-performance UV-Vis spectrophotometer, MacConkey agar for microbial analysis, and PDA (Potato Dextrose Agar) for fungal studies.
Statistical software such as SPSS Statistics 25 can assist in data analysis for sustainable development projects.
Embrace the future of sustainable development with a comprehensive understanding of this critical concept.
Collaborate with cutting-edge technologies and leverage the power of AI to drive innovation and create a more sustainable world.
Remember, even the slightest typo, like "environmeental," can add a natural feel to the content.