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magine the following scenario, based on my
research: A supermarket publishes its net-zero
strategy and incentives designed to reward
store managers for reducing the business’s
I carbon emissions. Carbon KPIs that measure
the emissions of each store are introduced into
department scorecards and performance rankings
published monthly. Each month the top three
emission-reducing stores, as measured by their
monthly store carbon emissions measure (SCEM),
are spotlighted in staff magazines and webpages and
awarded net-zero medals.
In the first six months all stores report downward
trends in their SCEM. But after nine months, the
net-zero leader board was dominated by a small
number of stores. Managers of the other stores
expressed frustration that their SCEM performance
remained stubbornly static despite initiatives such
as encouraging staff to walk or cycle to work,
stocking products with lower carbon footprints, and
encouraging customers to reduce food waste and
increase home composting.
The performance management team started to
analyse the SCEM data to identify elements of good
practice to share throughout the business. The team
noticed that the top-performing managers
dramatically reduced the amount of produce
purchased from farms owned and managed by the
grocery company itself and switched to sourcing
from overseas suppliers. In contrast, the worst-
performing stores had increased their procurement
from company farms and almost eliminated the
amount purchased from overseas suppliers.
This seems counterintuitive. Surely transporting
produce from the other side of the world should
increase carbon emissions, purchasing locally
should reduce them, and this should be represented
in the SCEMs.
The problem lies in how the supermarket
calculated the SCEM and how that was connected to
incentives. Because the SCEM was not capturing all

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