Doing Economics: Measuring Climate Change
Offered By: University of London International Programmes via Coursera
Course Description
Overview
This course will give you practical experience in working with real-world data, with applications to important policy issues in today’s society. Each week, you will learn specific data handling skills in Excel and use these techniques to analyse climate change data, with appropriate readings to provide background information on the data you are working with. You will also learn about the consequences of climate change and how governments can address this issue.
After completing this course, you should be able to:
• Understand how data can be used to assess the extent of climate change
• Produce appropriate bar charts, line charts, and scatterplots to visualise data
• Calculate and interpret summary statistics (mean, median, variance, percentile, correlation)
• Explain the challenges with designing and implementing policies that address climate change
No prior knowledge in economics or statistics is required for this course. No knowledge of Excel is required, except a familiarity with the interface and how to enter and clear data.
Syllabus
- Week 1
- This module explains why climate change is a serious global issue, and how we can use data to understand the extent of climate change.
- Week 2
- This module explores the effect of climate change on temperature variability and extreme weather events, and explains how we can use statistical summary measures to document changes in temperature distributions over time.
- Week 3
- This module uses data to summarise the relationship between CO2 emissions and global temperatures, explains methods of identifying causal relationships between two variables, and discusses the problem of spurious correlation.
- Week 4
- This module explains how policymakers can use survey data and models to evaluate climate-related policies, and discusses the relationship between environmental policy and environmental quality.
Taught by
Eileen Tipoe and Wendy Carlin
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