Lecture 27. Introduction to Sustainable Development goals Flashcards
3 SDG layers
- Environment
- Society
- Economy
Sustainability is meeting human needs with ecological constraints
The economy is making money while achieving sustainability
All layers are interrelated
Environment provides a base for well functioning society, and that in turn creates economic productivity
Environment goals
Clean water and sanitation
Life below water
Life on land
Climate action
4
Society goals
- No poverty
- Zero hunger
- Good health and wellbeing
- Quality education
- Gender equality
- Affordable and clean energy
- Sustainable cities and communities
- Piece, justice, and strong institutions
8
Economy goals
- Decent work and economic growth
- Industry innovation and infrastructure
- Reduced inequalities
- responsible consumption and production
4
what is the challenge with measuring SDGs globally?
It’s hard to have a consensus on how to measure the success of the goals
How if the Dahlgren and whitehead model related to the SDG?
Individual, community and environmental influences and choices that are made can affect the sustainability goals
- determinants of health act as the basis for goals in that sector
- actions do not need to act in isolation, but are interconnected
The Treaty’s position in the wedding cake(SDG)
imbedded through all layers centrally
SDGS and determinants of mental health
Also looks at proximal and distal factors
-can use many different measures to see how they influence particular factors
NZ living standards framework and SDGs
Living standards framework- well-being domains( a more subjective measure)
Almost all of the living standards frameworks can be connected to one or more SDGs
Some of the standards cannot be mapped out to the SDGs and also for SDG
Eg. Living standards measures: Leisure and recreation, Cultural identity, Social connections, and Subjective well-being do not map out to any SDGs
Goal5: Gender equality and goal 10: reduced inequalities also do not map out to any living standards
Goal 5 and 10 are very important and the reason they are not mapped in wellbeing standards is because they are key to all NZ legislations
Main difference between wellbeing and deprivation measures
- Wellbeing is a subjective measure
- Deprivation is more objective
Why are goal 5 and 10 not mapped to the living standards framework in NZ despite their importance?
Goals 5 and 10 are very important and the reason they are not mapped in wellbeing standards is that they are key to all NZ legislation
What is big data?
- Large or complex datasets, which often need terabytes or petabytes of storage.
- Large amounts of information at a population, regional or local level or span different geographical areas.
- Combining data from multiple sources to explore population health outcomes
used to inform SDGs
What makes data big? 4 Vs
- Volume: the computing capacity required to store and analyze data
- Velocity: the speed at which that data are created and analyzed.
- Variety: the types of data sources available (text, images, social media, administrative)
- Veracity: the accuracy and credibility of data, Can the data be recreated?
In addition, through the development of data science and other related research, there
are 3 additional ‘Vs’ of relevance:
• Variability: the internal consistency of your data, (e.g. reproducible research)
• Value: the costs required to undertake big data analysis should pay dividends for your organization and its patients.
• Visualisation: the use of novel techniques to communicate the patterns that would otherwise be lost in massive tables of data.
What makes data big? 4 Vs
- Volume: the computing capacity required to store and analyze data
- Velocity: the speed at which that data are created and analyzed.
- Variety: the types of data sources available (text, images, social media, administrative)
- Veracity: the accuracy and credibility of data, Can the data be recreated?
In addition, through the development of data science and other related research, there
are 3 additional ‘Vs’ of relevance:
• Variability: the internal consistency of your data, (e.g. reproducible research)
• Value: the costs required to undertake big data analysis should pay dividends for your organization and its patients.
• Visualisation: the use of novel techniques to communicate the patterns that would otherwise be lost in massive tables of data.
What makes data big? 4 Vs
- Volume: the computing capacity required to store and analyze data
- Velocity: the speed at which that data are created and analyzed.
- Variety: the types of data sources available (text, images, social media, administrative)
- Veracity: the accuracy and credibility of data, Can the data be recreated?
are 3 additional ‘Vs’ of relevance:
• Variability: the internal consistency of your data, (e.g. reproducible research)
• Value: the costs required to undertake big data analysis should pay dividends for your organization and its patients.
• Visualisation: the use of novel techniques to communicate the patterns that would otherwise be lost in massive tables of data.