Environmental and health impacts of small gasoline powered versus electric powered mobile outdoor equipment in Canada
Bibliographic record
Abstract
Lower-emitting electric alternatives exist for most SSI-engine equipment.This report seeks to inform ECCC's knowledge of the environmental and health impacts of common off-road SSI engine equipment and provide information on the feasibility of end-use electrification for common SSI engine equipment types. Scope of StudyThe scope of this study has two primary components: a review of current knowledge on the role of small spark-ignition engines in GHG and air pollutant emissions and exposures, and a comparison of cost and performance characteristics of gas-powered versus electric equipment. Review of Current Knowledge on Environment and Health Impacts of SSI EnginesThis includes analysis of emission inventory data for pollutants of concern, trends in GHG and air pollutant emissions, the geographic distribution of emissions in Canada, a comparison of data from the United States, and a broader review of how this subsector contributes to GHG and air pollutant emissions in Canada. Performance and Cost ComparisonThis information, presented in the section Equipment Cost, Performance, and Barriers to Electrification presents how gas-powered, corded-electric, and battery-electric equipment compare on the aspects of cost, performance, maintenance, and noise.This section includes an analysis of eight major types of off-road SSI engines: push lawnmowers, grass trimmers/bush cutters, leaf blowers/vacuums, snow blowers, lawn tractors, chainsaws, pressure washers, and portable generators.Collectively, these categories are estimated to represent about 90% of the population of SSI engines in Canada. MethodologyDeliverables were produced using "desktop" research to address the scope of the project.This included analysis of emissions data, literature reviews, and collecting cost/performance data from online sources. Analysis of Pollutant DatasetsThe section of the project regarding the role of SSI engines in GHG and air pollutant emission was completed through an analysis of public data sources and internally-produced modelling outputs provided directly by Environment and Climate Change Canada.These sources include Canada's National Inventory Report (NIR) for GHGs, Canada's Air Pollutant Emissions Inventory (APEI) for non-GHG pollutants, the US National Emissions Inventory (NEI), and NONROAD model outputs specific to SSI engines that were provided directly by ECCC. Review of Literature from Academia and Regulatory AuthoritiesPosterity Group conducted a review of existing literature on the subject of environmental and health impacts of SSI engines by seeking resources from the websites of relevant regulators (the U.S. EPA, CCOHS, CARB, and NIOSH) and then further conducting a citation review of studies that were found in order to identify other previous publications on similar topics. Survey of Retailer, Manufacturer, and Reviewer WebsitesTo obtain information regarding equipment capital and operating costs, performance characteristics, maintenance, power requirements, and noise level, Posterity Group relied on websites of retailers, equipment manufacturers, and equipment reviewers.The online stores of Canadian retailers Home Depot and Lowes were a primary source for cost and performance data due to the wide variety of equipment types and brands that they carry.Additionally, specialized stores and equipment reviews were used to document the most powerful electric options available for each equipment type.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".