Sustainable Community Involvement

Increase Student Greening Program to 3000 students

This indicator measures participation in the Student Greening Program.Explore the data
students
Current
3,000students
Dec 2020 Target
Goal Period ended December 2020

            What is this indicator?

            This indicator measures the number of Santa Monica College (SMC) students who participate in the Student Greening Program, known as the Student Sustainability Workshops.

            Why is it important?

            The Student Sustainability Workshops were first launched at SMC in 2002 to promote the college’s own progressive sustainability goals as well as the goals of the Santa Monica Sustainable City Plan. College students will be the next generation of policy makers and change agents; it is important for them to understand and embrace sustainable behaviors.

            This dataset is private.

            How are we doing?

            The Student Greening Program, which is housed at SMC’s Center for Environmental and Urban Studies, has grown tremendously in size, scope and popularity since 2002. As of 2014, the 2020 target of 3,000 students had already been achieved; 3,298 students have participated in the program. This is a significant increase from the 619 students in 2010.

            Program highlights include:

            • 50+ Student Workshop Leaders are trained and mentored to administer the workshops to their peers
            • Workshop participants contribute 2000+ volunteer hours to the community each academic year
            • Approximately 60 professors from 15 different academic disciplines support and promote the program
            • A significant percentage of program graduates become interested in pursuing green careers
            • 1000’s of sustainable behaviors are adopted by workshop participants, resulting in significant and continued conservation of resources

            How can we help?

            Learn more about the program here.

      Data Governance

      describes the quality of the data itself. Governance issues generally indicate that the data source is considered incomplete or unreliable.

      Model Health

      describes the quality of the predictive model. If the model health is poor, the trend prediction should not be trusted.

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