This interactive dashboard displays survey results from Brazil, China, Sweden, and the UK related to lifestyle changes that could help mitigate climate change.
The survey looked at respondents' beliefs on the importance of four potential lifestyle changes and their willingness to make these changes.
The data can be filtered by the respondents' gender, age, financial stability, environmental friendliness, and belief in collective efficacy.
The dashboard is easy to navigate and displays the data clearly.
Students should know how to read bar graphs.
Teachers should ensure students understand why these lifestyle changes can help slow climate change.
Teachers may need to explain the filter terms financial stability, environmental identity, and collective efficacy. Definitions for these terms are provided underneath the graphic.
Math classes can use this resource to practice interpreting graphs and summarizing data.
Cross-curricular connections can be made to science classes by having students research the lifestyle changes presented and their relation to climate change.
Students can use these questions to collect data for their class or community and create graphs to display their findings. Students can compare their class or communities data with that from the resource.
Other resources on lifestyle change include this article on living a low-carbon and equitable lifestyle, this personal account of deciding to live a fossil-fuel-free life, this Hot Mess video on the best ways to reduce your carbon footprint, and this video on choosing to live without a car.
Reduced air travel, meat consumption, and energy use are some of the climate activities that can lower CO2 levels, according to the resource, which focuses a study on the perspective of climate change from chosen countries. This resource is recommended for use in the classroom.
Statistical Reasoning: Statistics and Probability (6-8)
7.SP.A.1 Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences.