Managing Climate Change: Lessons from the U.S. Navy

Forest   Reinhardt; Michael W. Toffel

Article | Harvard Business Review

Managing Climate Change: Lessons from the U.S. Navy

by Forest Reinhardt and Michael W. Toffel

Abstract

The U.S. Navy operates on the front lines of climate change. It manages tens of billions of dollars in assets on every continent and on every ocean, which take many years to design and build and then have decades of useful life. This means that it needs to understand now what sorts of missions it may be required to perform in 10, 20, or 30 years and what assets and infrastructure it will need to carry them out. Put another way, it needs to plan for the world that will exist at that time. The navy is clear eyed about the challenges climate change poses. It knows that the effects of a warmer world will expand the geographic scope of its mission and increase demand for its military and humanitarian services. Climate change will also decrease its capacity to deliver those services, as the risk of damage to its bases and ports increases. This article examines the Navy’s approach to climate change and reflects on the implications for business.

Keywords: climate change; environment; military; disaster relief; refugees; Environmental Impact; environmental strategy; sustainability; Energy; Energy Conservation; Energy Sources; Energy Generation; Globalization; Innovation and Invention; Leadership; Strategic Planning; Problems and Challenges; Risk and Uncertainty; Supply Chain; Operations; Logistics; Infrastructure; Strategy; Environmental Sustainability; Service Industry;

Format: Print

Find at Harvard Register to Read

Citation:

Reinhardt, Forest, and Michael W. Toffel. "Managing Climate Change: Lessons from the U.S. Navy." Harvard Business Review 95, no. 4 (July–August 2017): 102–111.

Case | HBS Case Collection | September 2020 (Revised November 2020)

Student Success at Georgia State University (A)

Michael W. Toffel, Robin Mendelson and Julia Kelley

Georgia State University had developed a reputation for driving student success by nearly doubling its graduation rate for students of all racial, ethnic, and socioeconomic backgrounds. It did so while growing its student body and the proportion of Black/African American, low-income, and first-generation students—groups with historically lower postsecondary graduation rates compared to national averages. Georgia State’s Student Success team, led by Tim Renick and Allison Calhoun-Brown, used a data-based approach to deploy micro-grant programs to retain students, implemented predictive analytics to improve student advising, and optimized course sequencing to help students graduate before they exhausted their financial aid. In 2016, they faced a growing “summer melt” problem where nearly 20% of incoming students who committed to attend never actually enrolled at Georgia State—and many never enrolled at any college. At the same time, they wondered how to balance continuing to incrementally improve student success at Georgia State and scaling their efforts to help the many other universities facing similar problems who sought to learn from their experience.

Keywords: Education; Higher Education; Learning; Curriculum and Courses; Demographics; Diversity; Ethnicity; Income; Race; Leadership; Goals and Objectives; Measurement and Metrics; Operations; Organizations; Mission and Purpose; Organizational Culture; Outcome or Result; Performance; Performance Effectiveness; Performance Evaluation; Service Operations; Performance Improvement; Planning; Strategic Planning; Social Enterprise; Nonprofit Organizations; Social Issues; Wealth and Poverty; Equality and Inequality; Technology; Technology Platform; Education Industry; Atlanta;

Citation:

Toffel, Michael W., Robin Mendelson, and Julia Kelley. "Student Success at Georgia State University (A)." Harvard Business School Case 621-006, September 2020. (Revised November 2020.) View Details

Cite View Details Educators Purchase

This is a supplement to the Student Success at Georgia State University (A) case. The (B) case includes the results of a randomized control trial that Georgia State conducted to test education technology start-up AdmitHub’s chatbot solution as a strategy for improving “summer melt” (i.e., applicants confirming that they planned to enroll but then not enrolling).

The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools are then described: mean squared error (MSE), mean absolute deviation (MAD), Brier score, and cross-entropy, true/false positives/negatives, the confusion matrix, true positive rate (sensitivity or recall), false negative rate (Type II error rate), precision, true negative rate (specificity), false positive rate (Type I error rate), receiver operating characteristic curve (ROC) and area under the curve (AUC), and precision-recall curve.

Faculty & Research

Managing Climate Change: Lessons from the U.S. Navy

Abstract

Citation:

About the Authors

More from these Authors