November 8, 2018

By Dave Sawyer

With the recent federal carbon pricing plan, many were waiting with sharp pencils and “yah, but” insight to tear it apart. But they didn’t. As the government rolled out its well documented plan, it became clear that the underpinning data and analysis were credible. This was not some black box modeling forecast to 2030, but rather a transparent analysis using sound and publicly available data.

From the Ivory Tower, enough researchers had crunched the numbers using similar data to verify many of the government’s claims.  Data on emissions by province and subsector, household energy use and income are all publicly available, making it mostly straightforward to estimate household carbon costs net of the proposed federal rebates. Having trustworthy third parties who had done the math and dulled their pencils added to the credibility of the plan.

The press did not scoff, mostly, and did not give the opposing political view much space to counter punch. The National Post ran it on page one, with a good straightforward exposition of the nuts, bolts and impacts of the new Carbon Action Incentive.  All based on the data and analysis underpinning the announcement. Being able to do the math and replicate the results diluted the cynicism. 

Still, within the populace, many remain skeptical that carbon pricing rebates are a gimmick to pick pockets once again. If the policy is to be durable and survive successive federal elections, this popular skepticism will need to be routinely addressed with credible evidence.  And of course, the Auditor General will verify outcomes and assess progress against the government’s stated objectives, like revenue neutrality and the size of rebates.

Climate policy is clearly about so much more than the design of carbon pricing. Indeed, policy durability likely hinges as much on good policy design as it does on good governance.  

Climate policy necessitates wallowing in the data. Which is why a recent Statistics Canada workshop I attended seemed  a necessary step to improve the governance frame supporting the durability of the federal carbon pricing plan. Over a two-day period, Statistics Canada convened energy and GHG data users, asking how to make energy and GHG data better and more useable.  This effort is timely given both the on-going use of the data (Hello, Carbon Action Incentive) but also to address the increasing need to report on progress at home and abroad under the Paris Agreement. In short, the workshop was equal parts boring and essential to good climate governance.  

After thinking about the workshop objective, that of providing more useable GHG and energy data, I developed this narrative:

• Costs and distributional impacts rise fast with more decarbonization. Good analytics help reduce bad policy outcomes.
• The list of stock taking needs is long and growing. Modelling and analytics will be needed to assess plans and various outcomes under the Pan-Canadian Framework as well as reporting on progress internationally under our Paris Agreement commitments.
• Many and varied actors will clamour for data. Jumbled and piecemeal data leads to apple and orange comparisons, adding more confusion as disparate analytical sources get compared.

There are suggested GHG and energy data actions that can help policy making now:  

• Fix data mashup in household analysis. Most of the data to assess household carbon exposure by income and province is available. Yet, it could be better aligned with the variables necessary to calculate household impacts.    
• Build on the published emission intensity data, its helpful. GHG intensity, or GHGs per unit of product or economic value, underpins so much analysis.
• Provide more subsector resolution on the emission intensive and trade exposed industries. Impact assessments at the subsector level are now routinely conducted by the provinces and the federal government.

Yawn. Bored? Good, because the foundation of credible and durable GHG policy is boring data.  Wallowing in good, credible data will pay carbon dividends.