Linking spending decisions, greenhouse gases, and social games

Via the inbox, I get word of a company trying to develop an app/website to make the greenhouse gas impacts of your spending decisions plain.  Of course, they’d like your support to get started.

Many frustrated Americans would like to take climate action
into their own hands, but it’s hard to know where to start. Enter

“The basic idea is that every dollar we spend on products or services impacts the environment and society for better or for worse,” says Oroeco’s CEO, Ian Monroe, who also teaches courses on climate change and renewable energy at Stanford University. “The problem is that these impacts aren’t apparent when we’re deciding what to buy, particularly now that global supply chains have shifted problems half a world away. We are building a tool that automatically connects purchase data from debit and credit cards (via to scientific climate impact data – so you can track the climate footprint of your groceries, gas, airfare, home energy, clothing, etc. You can also see how you compare to your friends and earn points and prizes.”

There are lots of services to track home energy usage/impacts, like Opower and the now defunct but awesome Microsoft Hohm.  I’m not familiar with others that go beyond energy use to purchasing decisions.

Some communities try to inventory consumption impacts, like King County and the City of Minneapolis, but an app/game will probably be a lot more effective at reaching residents and consumers.

The geography and spatial allocation of greenhouse gas emissions

Researchers at Arizona State University have created a simulation to map CO2 emissions in cities to individual buildings and roadway segments (or “fine spatial and temporal quantification” in academic terms).  Called the Hestia Project, the simulation uses local data from buildings, air pollution reporting, and hour-by-hour traffic data to quantify emissions.

Cities are hungry for detailed emissions data, as in most cases what they can get from utilities is whole-city data, not broken down by neighborhood or building.

The idea is that while cities might be able to guess at where their CO2 emissions mostly come from, it’s more useful to know precisely where the hotspots are — a neighborhood of older houses, for example, or a handful of energy-wasting factories, or a frequently snarled intersection or merge point on a highway. By concentrating on these, a city could make significant improvements in its overall emissions picture with relative ease.

“We want to help them get the greatest reductions per dollar, the biggest bang for the buck,” said project leader Kevin Gurney, of Arizona State University.

A finer grain of detail will help target local emissions-reduction strategies.  However, as our emissions measurement tools get better, we need to make sure not to miss the key land use-transportation connection that drives a big portion of greenhouse gas emissions.  If a “frequently snarled intersection” looks like a significant emissions source in your community, the easy answer is widen the road, add some turn lanes, and voila, the source is reduced.  Freeway-widening might have a similar, short-term impact.  But adding capacity to the roadway to reduce total emissions will obviously backfire.

Another related issue deals with accounting or “responsibility” for emissions.  Suburban locations may look pretty green, since by comparison, not much of the regional travel occurs inside their boundaries.  All the “hotspots” are core freeways and intersections.  If we look at emissions based purely on geography, we miss the fact that suburban growth drives urban transport emissions.  That’s why in the community greenhouse gas accounting world, the newest methodologies use a “demand-based” method for accounting for transportation emissions, which more accurately assigns emissions to communities base on regional travel patterns.  It would be great to see the Hestia project “reassign” some of the roadway emissions to the origin and destination locations and see how the map colors change.