Contribution Analysis Toolkit Training: Part 3 - Data Interpretation Andrea kicks off. Hoi-Fei Mok, Mike Steinhoff, Eli Yewdall September 20, 2018

Data Interpretation

Department of Energy Cities-LEAP Cities Leading through Energy Analysis and Planning We applied for a grant from US DOE Cities LEAP program to help address this problem

Review Toolkit / Project Goal is to: Better understand drivers of change between inventories, Account for external sources of noise in the data, Get better indicators of overall progress to inform further CAP planning Toolkit contains an Excel workbook, data collection templates, lots of documentation. Available at icleiusa.org

Transportation Population growth Local programs Vehicle fuel economy VMT per person Transportation chart

Solid Waste Population growth GDP per person Incineration to landfilling shift Waste characterization shift Waste model difference Local programs Solid Waste chart

Weather Response Regression Analysis can predict what the use should be in response to hot/cold days Comparisons of predicted energy usage and actual inventory energy usage allow model to determine the proportion of weather impact and non-weather factors on energy usage. Temperatures are compared between inventory years to see impact of weather.

Weather Response Waterfall chart outputs are not an indication that overall trend between the inventory years is upward or downward, but that the specific years were warmer or cooler relative to each other. Outputs of the regression step can be used to consider climate change impact on community energy use

Application of Weather Response +   Summer (kWh/CDD) Fall (kWh/HDD) Spring (kWh/HDD) Winter (kWh/HDD) Constant (kWh/month) Coefficient 1.422 0.999 0.964 0.985 662.9 Error 0.433 0.359 0.239 0.079 25.1 T-Dist 0.001937451 0.005979392 0.000352544 7.04211E-11 9.31221E-17 From Workbook Regression Tab

Population Population chart includes impacts from transportation, waste, and residential electricity/fuel usage combined together Sector specific population impact can be found in the sector charts Population chart Residential electricity chart

Application of Population Increase Data Results can be used to create a useful indicator for comparison Population-Related Increases / New Resident Vs Baseline inventory MTCO2e / capita Are we growing ‘efficiently’ compared to baseline?

Residential Energy Weather Electricity fuel mix Electric heating Local programs Household growth kWh/therms per household Residential Electricity chart

Commercial Energy Weather Local programs Electricity fuel mix Commercial floor area/employee growth kWh/therms per ft^2/employee Commercial Electricity chart

Activity Intensity Changes Per capita or per employee changes are determined after accounting for weather. The net effect of everything else occupant behavior, changes to building types and uses, federal appliance standards, utility efficiency programs, etc If no specific programs are accounted for this is the best indicator of are we collectively as a community doing better or worse.

Program Impacts Including specific Program Impacts will explain a portion of the remaining activity intensity change Allocation of Programs from Un-attributed Change

Allocation of Programs that Exceed Un-attributed Change Program Impacts Note that the analysis is constrained by the total change that happened between the two inventories If your program impact is bigger than the net additional change, the remaining change to intensity would flip direction. “…After accounting for our programs, residents used more energy Allocation of Programs that Exceed Un-attributed Change

Not for Forecasting There is a tendency to want to use the trends of the past to predict the future Two observations is no basis for a trend, this is not recommended. Lots of year-to-year variability that you don’t want to extrapolate

National Trends

ClearPath data many of the inventories are unverified, though some care was taken to ensure that the individual records analyzed were complete and without obvious error. This means that individual communities may have fallen out of different aspects of the analysis. all data used is self-reported and should be considered in light of the typical data challenges that many communities face when doing this work, such as inconsistencies in utility data and the fact that most transportation data is modeled, not measured. To reduce the amount of skew those cities have on the overall results, only a single pair of inventories were analyzed from each city. Where multiple inventories from a single community were removed, the earliest and most-recent were selected to remain in the analysis.

Key Findings: Residential and Commercial Electricity Looking at Portland, OR, commercial electricity emissions from 2005 to 2016 decreased by over thirty percent; the overall emissions trend very closely follows the emissions factor. However, employment growth of about fifteen percent was slightly more than offset by a steady decrease in usage per employee; without this decrease, the overall emissions decrease would have been much less. Finding 1: Both a cleaner electric grid and efficiency have important parts in offsetting growth and reducing emissions.

Key Findings: Residential and Commercial Electricity Had more data going back to 2000, so was able to see a longer trend line. May have contributed to the actual emissions line decoupling from the emissions factor line. Finding 1: Both a cleaner electric grid and efficiency have important parts in offsetting growth and reducing emissions.

Key Findings: Residential and Commercial Electricity Hypothetical to represent holding the behavior change constant while still having the weather impact, emissions factor, and population growth. Finding 1: Both a cleaner electric grid and efficiency have important parts in offsetting growth and reducing emissions.

Key Findings: Residential and Commercial Electricity Energy efficiency score is based on data from the ACEEE state energy efficiency scorecard. Only chose the building code and one pair of inventories were selected from each city and the annual CO2 change was calculated per pair, ranked by state. CA is the biggest rank line. Statistical significant correlation between state energy efficiency policy and commercial building. Finding 2: State efficiency policies have a noticeable effect on changes in commercial energy use per employee

Key Findings: Residential and Commercial Electricity For residential energy use, the relationship between usage change and efficiency scores is not statistically significant.

Key Findings: Residential and Commercial Electricity Anything below 0 means there was a decrease in residential electricity emissions Residential electricity percent change with population and per-capita usage component

Key Findings: Residential and Commercial Electricity Anything below 0 means there was a decrease in residential electricity emissions Greater shift in negative emissions seen, but behavior change is still important Emissions factor may also mask lack of internal progress and fail to send the right local policy signals Residential electricity percent change with population, per-capita usage component, and emissions factor

Key Findings: On-Road Transportation Finding 1: Both more efficient vehicles and reduced vehicle miles per person have important parts in offsetting growth and reducing emissions.

Key Findings: On-Road Transportation Dip at 2007-08 is from a switch to biofuel Finding 1: Both more efficient vehicles and reduced vehicle miles per person have important parts in offsetting growth and reducing emissions.

Key Findings: On-Road Transportation Finding 2: Across communities, there is a range of changes in transportation emissions, though a majority show decreasing emissions.

Key Findings: On-Road Transportation Finding 2: Across communities, there is a range of changes in transportation emissions, though a majority show decreasing emissions.

Summary: State and Federal Action to clean the grid and improve transport efficiency are important Yes, local action makes a difference, even if external factors seem much bigger Both are necessary!

Thank You Hoi-Fei Mok Hoi-fei.mok@iclei.org Michael Steinhoff Michael.steinhoff@iclei.org Eli Yewdall Eli.yewdall@iclei.org ICLEI USA Headquarters 1536 Wynkoop St #901 Denver, CO 80202 (510) 844-0699 icleiusa.org @ICLEI_USA