It Takes a Network to Tackle Growth and Complexity
Social network analysis may benefit regional conservation efforts in the Texas Hill Country and help mitigate other challenging societal issues.
Social network analysis may benefit regional conservation efforts in the Texas Hill Country and help mitigate other challenging societal issues.
Meeting today’s growing conservation challenges requires that we find new ways of thinking about and practicing conservation, rooted in solving social problems through scalable methods and prototypes that deliver results.
The key to creating a vibrant and sustainable company is to find ways to get all employees personally engaged in day-to-day corporate sustainability efforts.
A free food project on New York's waterways challenges residents to imagine how we might adapt to a more resource-constrained world.
During a critical period in its history, Greenpeace restructured its organization in order to leverage gains made at a local level.
The key to creating a vibrant and sustainable company is to find ways to get all employees personally engaged in day-to-day corporate sustainability efforts.
The era of corporations integrating sustainable practices is being surpassed by a new age of corporations actively transforming the market to make it more sustainable. Open access to this article is made possible by The Regents of the University of Michigan on behalf of the Erb Institute.
For much of its history, Wal-Mart’s corporate management team toiled inside its “Bentonville Bubble,” narrowly focused on operational efficiency, growth, and profits. But now the world's largest retailer has widened its sights, building networks of employees, nonprofits, government agencies, and suppliers to “green” its supply chains. Here's how and why the world’s largest retailer is using a network approach to decrease its environmental footprint – and to increase its profitability.
To do as much good as possible with limited resources, funders should look to woefully underfunded protest movements.
Using artificial intelligence to predict behavior can lead to devastating policy mistakes. Health and development programs must learn to apply causal models that better explain why people behave the way they do to help identify the most effective levers for change.