E-22023 DSM Programs Evaluation Reports
115 passages
Surveys and Interviews This subsection describes the data-collection activities conducted for impact evaluations. It should be noted that surveys and interviews were often integrated to collect impact, process, and market information as di...
AI summary This subsection outlines the data collection methods used for impact evaluations, focusing on participant surveys conducted between August and December 2023. Surveys were used to gather information on free-ridership and spillover effects, with 392 participants surveyed, primarily via telephone, except for the Instant Savings program which used an online survey.
Table 2: 2023 Participant Surveys Program Component Participants Residential Instant Savings 122 Home Energy Assessment 100 Green Heat 124 BNI Small Business Energy Solutions 46 Total 392 › In-depth interviews with program staff, participa...
AI summary Table 2 outlines the number of participants in various programs in 2023, including residential and business initiatives. In-depth interviews were conducted between August 2023 and January 2024 to evaluate program impact, free-ridership, and spillover effects.
Net-to-gross Assessment and Net Savings Calculations Free-ridership levels were established for most program components by conducting self-report surveys or indepth interviews. These surveys and interviews included a set of questions used...
AI summary The document discusses the methodology used to assess free-ridership levels for various energy efficiency programs, including the use of self-report surveys and interviews. Free-ridership levels were updated for several programs in 2023, and the methodology considers the impact of previous participation in other program components.
Home Energy Assessment - › HEA saw an increase in participation of 47% and an increase in average gross savings per home of 57% compared to 2022, likely driven by the integration of the Canada Greener Homes Grant (CGH Grant). - › Free-ride...
AI summary The Home Energy Assessment (HEA) program saw increased participation and savings, likely due to the Canada Greener Homes Grant. Free-ridership levels for energy efficiency upgrades decreased from 26% in 2020 to 17% in 2023, while solar PV free-ridership remained at 26%. Savings were 8% and 13% higher than those tracked by EOne. Billing analysis results were inconclusive, so 2018 overestimation ratios were retained.
Green Heat - › Green Heat participation levels decreased by 11% compared to 2022, particularly for MSHPs (22% decrease). Conversely, MSHP installations incented through HEA increased over the same period (116% increase) due in large part t...
AI summary Green Heat participation levels dropped by 11% in 2023, especially for MSHPs, but installations incentivized by HEA increased significantly. Free-ridership for Green Heat measures rose, particularly for MSHPs. Net energy and peak demand savings were lower than EOne's tracked savings due to updated NTGRs for biomass, MSHP, and demand reduction measures.
Custom The overall key findings of the Custom impact evaluation were as follows: - › Compared to 2022, Custom participation decreased in 2023 due in part to the lack of participation in the Pay-for-Performance (P4P) service and the removal...
AI summary The Custom impact evaluation found a decrease in participation in 2023 compared to 2022, partly due to the lack of Pay-for-Performance service and the removal of the Retrofit Operational Demand Reduction offer. Free-ridership levels increased for Retrofit and solar PV projects, while New Construction free-ridership remained high. Energy and peak demand savings were adjusted by the Evaluator, resulting in lower savings than those tracked by EOne.
Small Business Energy Solutions - › The number of completed projects in Small Business Energy Solutions decreased by 27% in 2023, while average gross energy savings per project remained flat compared to 2022. - › Following the desk reviews...
AI summary The number of completed Small Business Energy Solutions projects dropped by 27% in 2023, with average energy savings remaining flat. Adjustments were made to energy and peak demand savings for lighting and MSHPs based on evaluations. Free-ridership increased by 5% in 2023, primarily due to lighting projects. Evaluated energy savings slightly differ from tracked savings.
3.2 Net-to-gross Ratio The NTGRs outlined in [Table](#page-30-0) 7 below were applied to gross savings to estimate net savings. NTGRs were established based on the free-ridership levels and spillover in some cases. Based on the established...
AI summary The document discusses the Net-to-gross Ratio (NTGR) used to estimate net savings from energy efficiency programs. NTGR is calculated using free-ridership and spillover levels, with the formula NTGR = (1 – % Free-ridership + % Spillover). The methodology relies on self-reporting and examples are provided in Appendix III.
Table 7: 2023 Free-ridership, Spillover, and NTGRs Program Component and Measure Type Free-ridership Levels Spillover Levels NTGR Residential Appliance Retirement Refrigerators 43% 0% 0.57 Freezers 45% 0.55 Air Conditioners 47% 0.53 Small...
AI summary Table 7 presents data on free-ridership, spillover, and net-to-gross ratios (NTGRs) for various energy efficiency programs in 2023, highlighting levels of free-ridership and spillover across different measures and participant categories, with NTGRs indicating the proportion of savings attributable to the program.
This appendix presents some examples of how the Evaluator established the margins of error for participant surveys, adjustment ratios, installation rates, and free-ridership levels. For participant survey margins of error, the example is d...
AI summary This appendix provides examples of how the Evaluator determined margins of error for various metrics, including participant surveys, adjustment ratios, installation rates, and free-ridership levels, using evaluations from the 2023 Home Energy Assessment, Small Business Energy Solutions DIY path, and Green Heat mini-split heat pump programs.
Free-ridership Level Margin of Error The Evaluator used the margin of error calculation of the free-ridership level for Green Heat MSHP in 2023 as an example. Below are the steps followed to calculate this margin of error.
AI summary The Evaluator provided an example of calculating the margin of error for the free-ridership level of Green Heat MSHP in 2023, outlining the steps taken in the process.
Calculation of the Weighted Average of Free-ridership Levels The weighted average was calculated by using the following formula. $$Weighted\ Average\ FR = \frac{\sum_{i=1}^{n} Free - ridership\ Level_{i} \times Revised\ Gross\ Energy\ Savi...
AI summary The weighted average free-ridership level was calculated using a formula that considers the free-ridership level and revised gross energy savings for each participant who installed a MSHP. The result was determined to be 14%.
Calculation of the Standard Error Since the free-ridership level is based on a weighted average, the Evaluator also calculated a weighted standard error of the free-ridership level instead of a simple standard error. The same formula as fo...
AI summary The document explains the calculation of the weighted standard error for the free-ridership level, using a formula that considers the weighted average of free-ridership savings and gross savings per participant. The result of this calculation is 0.0344.
Calculation of the Margins of Error The margin of error on the adjustment ratio of the Green Heat MSHP free-ridership level was established by using the following formula that is the general equation linking that standard error to the marg...
AI summary This text explains the calculation of the margin of error for the free-ridership level of the Green Heat MSHP program in 2023, using a formula involving standard error, a t-coefficient, and a finite population correction factor. The margin of error was determined to be 5.5%.
NTGR Calculations Free-ridership algorithm Low Free- ridership Participant High Free- ridership Participant Average Free- ridership Participant Questions Calculation Algorithm Answer Calc. Result Answer Calc. Result Answei Calc. Result D3....
AI summary The text discusses a free-ridership algorithm used in a regulatory proceeding, focusing on a question regarding whether participants would have paid the entire cost of equipment without a rebate. The calculation algorithm is based on a scale from 0 to 10, with results calculated as a percentage of the answer.
Instant Savings Findings and Recommendations This subsection presents the key findings and recommendations from the Instant Savings evaluation. 2023 Instant Savings Finding: Instant Savings achieved both of its energy savings and its peak...
AI summary The 2023 Instant Savings program achieved its energy and peak demand savings targets, with increased participation and satisfaction from retailers. However, free-ridership for LED lamps and fixtures increased, while spillover for LED fixtures decreased. Net energy savings were 14% lower than initially tracked due to updated spillover and free-ridership levels. A recommendation is made to review and phase out outdated LED Non-A type lamp models.
3.3.1 Free-ridership and Secondary Market Impacts For appliance retirements, free-ridership corresponds to the energy consumption of appliances that would have been disposed of in the absence of the program, and secondary market impacts co...
AI summary This section discusses the methodology used to estimate free-ridership and secondary market impacts for appliance retirements in Nova Scotia. Due to the lack of 2023 data, 2022 and 2018 data were used for different appliance types. The calculation algorithm is detailed in Appendix II, with results summarized in Table 12.
Table 12: 2023 ARet Free-ridership Levels Appliance Type Free-ridership (with Secondary Market Impacts) Level Margin of Error Refrigerators 43% 6.3% Freezers 45% 5.5% Air Conditioners 47% 13.0% Small Refrigerators 32% 10.3% Small Freezers...
AI summary Table 12 presents free-ridership levels for various appliance types in 2023, including refrigerators, freezers, air conditioners, and others, with percentages and margin of error. Section 3.3.2 discusses participant spillover, indicating the impact of program participation beyond direct beneficiaries.
3.3.3 Net-to-gross Ratio Calculation NTGR values were calculated using the following equation. $$NTGR = (1 - \% Free-ridership + \% Spillover)$$ Using the average free-ridership levels established for each appliance type, the Evaluator cal...
AI summary The NTGR values were calculated using the equation NTGR = (1 - % Free-ridership + % Spillover), based on average free-ridership levels for each appliance type. The results are presented in Table 13.
Table 13: 2023 ARet Effects and NTGR Appliance Type Average Free-ridership (with Secondary Market Impacts) Spillover NTGR Refrigerators 43% 0% 0.57 Freezers 45% 0.55 Air Conditioners 47% 0.53 Small Refrigerators 32% 0.68 Small Freezers 32%...
AI summary Table 13 presents the 2023 ARet Effects and NTGR for various appliance types, including free-ridership rates and spillover impacts. The table indicates that refrigerators, freezers, and air conditioners have higher free-ridership rates compared to smaller appliances like small refrigerators and freezers, with dehumidifiers showing no free-ridership.
Table 18: 2023 Instant Savings Evaluation Approach Evaluation Objective Research Question Methodology Collect information on participant and partner perspectives › What is the awareness level about Instant Savings and how did participants...
AI summary This section outlines the 2023 Instant Savings Evaluation Approach, detailing how participant and partner perspectives are collected, how gross and net results are calculated, and the methods used, such as online surveys and tracking sheet audits.
Note on Margin of Error For evaluation activities that yield quantitative results based on a sample, the Evaluator aimed to achieve a maximum margin of error of 10% at a confidence level of 90%. This means that, if measurements were conduc...
AI summary The document discusses the margin of error in quantitative evaluations, aiming for a maximum 10% margin at a 90% confidence level. It explains that this margin reflects precision, not accuracy, and notes that it only accounts for random sampling errors, not non-sampling errors or biases.
8.3.1 Free-ridership The free-ridership level is calculated using algorithms that assess the likelihood for participants to have purchased LED lamps or fixtures without the discount offered through Instant Savings. The algorithms consider...
AI summary The free-ridership level for the Instant Savings program is calculated using algorithms and a self-reporting survey, with separate calculations for Costco and non-Costco participants due to insufficient survey responses from Costco. The methodology used is consistent with previous evaluations in 2020 and 2021.
Measurement of Free-Ridership for Non-Costco Participants The free-ridership results for non-Costco participants are presented in [Table](#page-111-0) 26 below. 26 At the time of writing, 2023 data were not yet available. The Nova Scotia-s...
AI summary The free-ridership results for non-Costco participants are presented in a table, with data sourced from Nova Scotia Power and Emera Inc. The 2023 data were not yet available at the time of writing, and the Nova Scotia-specific factor was derived from 2022 emissions and generation data.
Table 26: 2023 Instant Savings Free-ridership for Non-Costco Participants Free-ridership Product Category Level Margin of Error LED Lamps 70% 6.3% LED Fixtures 48% 10.3% All Other Products - - For non-Costco participants, the intercept sur...
AI summary The table shows that in 2023, free-ridership for non-Costco participants in the Instant Savings program was 70% for LED lamps and 48% for LED fixtures, both higher than the 2021 levels. This indicates an increase in the proportion of customers who benefited from the program without participating directly.
Treatment of Free-Ridership for Costco Participants Given the relatively large portion of lighting sales from Costco and indications based on 2019 data that Costco consumers may have different behaviours than consumers at other retailers,...
AI summary The Evaluator determined a free-ridership level for Costco consumers of LED lamps and fixtures using 2022 data from an online survey, employing the same methodology as in 2023. This was done due to Costco's significant share of lighting sales and potential differences in consumer behavior compared to other retailers.
Product Category Free-Ridership Level Margin of Error LED Lamps 36% 8.2% LED Fixtures 50% 5.0% Overall 2023 Free-Ridership Levels
AI summary The document presents free-ridership levels for LED lamps and fixtures in 2023, with LED lamps showing a free-ridership level of 36% and LED fixtures at 50%, each with their respective margins of error.
The overall free-ridership levels for LED lamps and fixtures correspond to the average of the 2022 Costco free-ridership levels and the free-ridership of the non-Costco consumers obtained in 2023, weighted by the number of units sold in 20...
AI summary The text discusses free-ridership levels for LED lamps and fixtures, combining data from Costco and non-Costco consumers in 2022 and 2023. It mentions a 56% free-ridership for LED recessed downlight fixtures and 33% for other LED fixtures for non-Costco participants in 2021, with an overall free-ridership level of 45% for LED fixtures.
Table 28: 2023 Instant Savings Free-Ridership for LED Lamps and Fixtures Product Potoilor Free-ridership Evaluation Number of Units Weighted Average Free-Ridership Category Retailer Period Level Margin Error Sold in 2023 Level Margin of Er...
AI summary Table 28 presents free-ridership evaluation data for LED lamps and fixtures sold in 2023 through the Instant Savings program, including free-ridership levels and margin of error for Costco and non-Costco retailers. The Evaluator considered free-ridership values for all other products rebated through Instant Savings to be nil.
The NTGR is calculated using the following equation: NTGR = (1 – % Free-ridership + % Spillover) Using this equation, the NTGRs for LED lamps and fixtures were estimated at 0.50 and 0.53 respectively, as presented in [Table](#page-113-2) 3...
AI summary The NTGR is calculated using the equation (1 – % Free-ridership + % Spillover). For LED lamps and fixtures, NTGRs were estimated at 0.50 and 0.53, respectively. For other products, an NTGR of 1.00 was used due to difficulties in assessing free-ridership and spillover levels for non-LED products.
9 Instant Savings Key Findings and Recommendations As mentioned previously, the main objectives of the 2023 Instant Savings evaluation were as follows: - › Collect information on participant and partner perspectives. - › Calculate gross an...
AI summary The 2023 Instant Savings program exceeded its energy and peak demand savings targets, with 11.476 GWh and 1.459 MW achieved, respectively. Participation increased by 9%, with notable growth in controls and appliance sales. LED lighting remained the primary source of savings, but non-LED replacements are becoming less available. Free-ridership levels for LED lamps and fixtures also increased.
Net energy savings calculation without spillover (for each appliance) 2 d Have Disca Scena Acquir Id-Be er Finds ernative inds Alternative Type Alternative Type Overall Proportion Final Energy C Net Unitary Savings U nit ocen ai 10 nit Fro...
AI summary The document presents a table illustrating net energy savings calculations for appliance retirements, specifically focusing on refrigerator retirements in 2022. The table includes data on energy savings with and without spillover effects and a formula for calculating free-ridership.
Table 1: Overview of Data Collection Activity Descriptor This Instrument Instrument Type Participant Intercept Survey with Online Option Estimated Time to Complete 5 min Target Audience Instant Savings participants who have purchased LED n...
AI summary The document outlines data collection activities for a survey targeting participants of the Instant Savings program who purchased LED bulbs or fixtures during the fall 2023 campaign. The survey aims to collect data on usage, free-ridership, cross-influence, and participant awareness. It is to be adapted by Econoler.
D. Free-Ridership - LED Bulbs - D1. Efficiency Nova Scotia offered a discount on non-pear-shaped LED bulbs. Before paying at the cash register, were you aware that a discount was offered on the purchase of LEDs? [ALLOW ONLY ONE CODE] - 1....
AI summary This section of the proceeding focuses on assessing whether participants were aware of a discount offered on non-pear-shaped LED bulbs by Efficiency Nova Scotia before making a purchase. The questions aim to determine awareness and subsequent actions based on that awareness.
G. Free-Ridership – LED Fixtures - G1. Efficiency Nova Scotia offered a discount on LED fixtures. Before paying at the cash register, were you aware that a discount was offered on the purchase of LED fixtures? [ALLOW ONLY ONE CODE] - 1. Ye...
AI summary This section of the regulatory proceeding document addresses free-ridership related to LED fixtures, asking respondents if they were aware of a discount offered by Efficiency Nova Scotia on LED fixtures and how that influenced their purchasing decisions.
APPENDIX IX Instant Savings Algorithms for Free-ridership Calculations
AI summary This appendix details the algorithms used for calculating free-ridership in the context of instant savings, likely related to energy efficiency programs.
Algorithm – LED Bulbs Section 1 – Free-ridership level for those who were unaware of discount before paying D1. Before paying at the cash register, were you aware that a discount was offered on the purchase of LEDs? IF Yes : GO TO Section...
AI summary This section outlines an algorithm to determine free-ridership levels for LED bulb discounts. It asks respondents about their awareness of discounts before purchasing and whether they postponed purchases due to the program.
DEFINITIONS Accuracy Reflects the proximity of measurements to the true value. 3 HE A F Participant Perspective 8 3.1 Sa itisfaction with HEA 8 3.2 Su ggestions for Improvement 9 4 HE A lı mpact Evaluation 10 4.1 Tra acking Sheet Audit 10...
AI summary The document outlines definitions and evaluation methods related to home energy assessments (HEA), including accuracy, participant satisfaction, energy savings, peak demand savings, and net-to-gross ratio calculations. It also discusses the evaluation of Green Heat programs and their impact.
HEA Findings and Recommendations 2023 HEA Finding: Both HEA net electrical energy and demand savings exceeded targets. 2023 HEA Finding: The significant increase in participation and average gross savings per home observed since the introd...
AI summary The 2023 HEA findings show that energy and demand savings exceeded targets, with increased participation and savings per home since the introduction of the Canada Greener Homes Grant. The HEA is well-received, and the evaluation found higher savings due to lower free-ridership. Billing analysis results were inconclusive, and the ORs from 2018 will not be used beyond 2023. A new evaluation approach for 2024 is recommended.
less satisfied with the following components of the Green Heat program: the rebate, the Green Heat website, communications with EOne, a lack of marketing materials, and the list of eligible equipment. Recommendation #1: Ensure that Green H...
AI summary The Green Heat program faces challenges with rebate satisfaction, website usability, communication with EOne, and participant awareness. Recommendations include improving marketing materials, enhancing the website, and improving communication. Free-ridership levels are also a concern, especially for MSHPs.
Table 6: 2023 HEA Evaluation Approach Evaluation Objectives Research Questions Methodology Collect information on participant perspectives › What is the level of satisfaction among participants? › What participants suggest for improving th...
AI summary The 2023 HEA Evaluation Approach outlines objectives to collect participant perspectives, calculate gross and net results, and includes methodologies such as participant surveys, tracking sheet audits, billing analysis, and GHG emission reduction calculations.
4.3.1 Free-ridership For HEA, free-ridership occurs when participants would have implemented energy efficiency upgrades and/or solar PV systems in the absence of the program component. Free-ridership was assessed as part of this evaluation...
AI summary The document discusses free-ridership in the Home Energy Assessment (HEA) program, noting a 17% free-ridership rate for energy efficiency upgrades and 26% for solar PV systems. The assessment used a self-reporting approach and telephone surveys, with changes in methodology in 2023. The introduction of the CGH Grant is suggested to have influenced participation levels.
Table 17: 2023 HEA Free-ridership Level Measure Average Free-ridership Level Sample Size Population Size Margin of Error Energy Efficiency Upgrades 17% 77 3,995 4.20% Solar PV Measures 26% 29 1,520 6.50% 4.3.2 Participant Spillover
AI summary Table 17 presents the free-ridership levels for energy efficiency upgrades and solar PV measures under the 2023 HEA program. The data indicates that 17% of energy efficiency upgrades and 26% of solar PV measures were free-riders, with respective sample sizes and margins of error provided. Section 4.3.2 discusses participant spillover.
For HEA, participant spillover occurs when participants implement additional measures recommended in their initial energy assessments after their participation in the program component, i.e. after having completed the final energy assessme...
AI summary The document discusses spillover in the Home Energy Assessment (HEA) program, where participants implement additional energy efficiency upgrades after completing the program. A spillover level of 1% was measured in 2023, consistent with the 2020 measurement, based on participant surveys.
The NTGR is calculated using the following equation. NTGR = (1 – % Free-ridership + % Participant Spillover) The NTGR values calculated with the free-ridership and spillover levels established through the 2023 HEA participant survey are pr...
AI summary The NTGR is calculated using a formula that accounts for free-ridership and participant spillover, with values derived from the 2023 HEA participant survey and presented in Table 19.
5 HEA Key Findings and Recommendations As mentioned previously, the main objectives of the 2023 HEA evaluation were as follows: - › Collect information on participant perspectives - › Calculate gross and net results, namely electrical firs...
AI summary The 2023 HEA evaluation found that the program exceeded energy and demand savings targets, with increased participation and average savings per home. Participant satisfaction was high, though delays in rebate processing were noted. Free-ridership levels were lower than previously tracked, and billing analysis results were inconclusive, prompting recommendations for further exploration and a revised evaluation approach for 2024.
Table 25: 2023 Green Heat Evaluation Approach Evaluation Objectives Research Questions Methodology Collect information on participant and partner perspectives › What is the level of satisfaction with the component among participants and co...
AI summary This section outlines the 2023 Green Heat Evaluation Approach, focusing on participant and partner perspectives, calculating gross and net results, and analyzing the market evolution of MSHPs. The methodology includes surveys, interviews, and data audits to evaluate program effectiveness and identify areas for improvement.
A total of 124 Green Heat participants composed of n=90 MHSP participants, n=29 biomass participants, and n=5 ETS participants took part in a telephone survey in October and November 2023. The average interview duration was 17 minutes. The...
AI summary A telephone survey of 124 Green Heat participants, including 90 from MHSP, 29 from biomass, and 5 from ETS, was conducted in October and November 2023 to gather inputs for calculating freeridership and spillover effects and to understand participant perspectives. The survey results and methodology are detailed in Appendices IX and X, with noted high margins of error for biomass and ETS due to small sample sizes.
Note on Margin of Error For evaluation activities that yield quantitative results based on a sample, the Evaluator aimed to achieve a maximum margin of error of 10% at a confidence level of 90%. This means that, if measurements were conduc...
AI summary This section discusses the margin of error in evaluations of energy programs, specifically the Green Heat initiative. It outlines the 10% margin of error at a 90% confidence level, explaining that it reflects random sampling errors and does not account for non-sampling errors or biases.
9.3.1 Free-ridership For Green Heat, free-ridership occurs when participants would have still installed a new, more efficient heating system in the absence of the program component. Free-ridership was assessed during the 2023 evaluation us...
AI summary The document discusses free-ridership in the Green Heat program, noting that 48% of participants who installed MSHPs had already decided to do so before the program, an increase from 39% in 2022. Free-ridership for biomass heating systems was 47%, up from 41% in 2021. Demand reduction measures had a lower free-ridership of 9%, but this is based on a small sample size.
Table 32: 2023 Green Heat Average Free-ridership Levels Measure Average Free-ridership Level Sample Size Population Size Margin of Error MSHPs 48% 90 1,132 5.5% Biomass 47% 29 213 8.2% Demand Reduction 9% 5 314 13.4% CASHPs and GSHPs 33% 3...
AI summary Table 32 presents the 2023 average free-ridership levels for various Green Heat measures, including MSHPs, Biomass, Demand Reduction, and CASHPs and GSHPs. The data indicates that free-ridership levels vary significantly across different measures, with MSHPs having the highest level at 48% and Demand Reduction having the lowest at 9%. The results are based on a sample size and margin of error for each measure.
9.3.2 Net-to-gross Ratio Calculation The NTGR was calculated using the following equation. $$NTGR = (1 - \% Free-ridership)$$ Using the free-ridership level established for each measure category, the Evaluator calculated the NTGR values pr...
AI summary The Net-to-Gross Ratio (NTGR) is calculated using the formula (1 - % Free-ridership), with free-ridership levels established for each measure category. The Evaluator used these levels to calculate the NTGR values presented in Table 33.
10.2.5 Measured Free-ridership The Evaluator analyzed the free-ridership level, the results of which are presented in [Table](#page-67-0) 38 below, to gain insights into the evolution of MSHP adoption rates in Nova Scotia. The MSHP free-ri...
AI summary The Evaluator analyzed free-ridership levels for MSHPs in Nova Scotia, noting stability from 2017 to 2020, a 39% level in 2021 due to an algorithm update, and an increase to 48% in 2023, indicating growing consumer awareness and interest in MSHPs.
Table 38: Green Heat Free-ridership Levels for MSHPs, 2017-2023 Free-ridership 2017 2018 2019 2020 2021 2022 2023 MSHPs 51% 54% 53% 53% 39% 39% 48% 37 As part of the American Innovation and Manufacturing Act of 2020, restrictions on the us...
AI summary Table 38 shows the free-ridership levels for Mini-Split Heat Pumps (MSHPs) from 2017 to 2023, with percentages ranging from 39% to 54%. A footnote mentions the American Innovation and Manufacturing Act of 2020, which will impose restrictions on hydrofluorocarbons in HVAC equipment in the U.S. starting in 2025.
tion numbers. 2023 Green Heat-Finding: MSHP participant awareness of time-of-day rates is relatively high, but there is still room to increase awareness about ETS and participation in Green Heat. A total of 61% of MSHP participants said th...
AI summary The 2023 Green Heat-Finding highlights that while awareness of ETS technology and time-of-day rates is relatively high among MSHP participants, participation in Green Heat remains low. Free-ridership levels for MSHPs have increased significantly to 48%, compared to 39% in 2021, while biomass measures saw a smaller increase to 47%.
14.3.1 Free-ridership For EPI, free-ridership occurs when participants would have installed the same energy efficient products in the absence of the program component. For low-income participants, the free-ridership level is assumed to be...
AI summary The document discusses free-ridership in the context of the Efficient Product Installation (EPI) program. Free-ridership occurs when participants would have installed energy-efficient products without the program. For non-low-income participants, free-ridership was assessed in 2021 via a telephone survey, but no new data was collected in 2023. The same free-ridership levels from 2021 were applied to the smart thermostat for electric baseboard pilot measure.
Table 52: 2023 EPI Participant Free-ridership Levels Average Free-ridership Level Margin of Error Low-income Participants All Products 0% N/A Non-low-income Participants LED Lighting 25% 3.6% Low-flow Showerheads 10% 3.0% Other Products 0%...
AI summary Table 52 presents the 2023 free-ridership levels for EPI participants, showing that non-low-income participants had 25% free-ridership for LED lighting and 10% for low-flow showerheads, while low-income and other product categories had 0% free-ridership. Section 14.3.2 discusses participant spillover.
The NTGR values were calculated using the following equation: $$NTGR = (1 - \% Free\text{-ridership} + \% Internal Spillover)$$ Overall NTGR values were calculated using the free-ridership and spillover levels established per participant t...
AI summary The NTGR values were calculated using an equation that considers free-ridership and internal spillover levels, with overall NTGR values determined based on participant type, product category, and the proportions of savings associated with each.
Home Energy Assessment Appendix I HEA Participant Survey Questionnaire Appendix II HEA Participant Survey Results Appendix III HEA Tracking Sheet Audit Appendix IV HEA Detailed Methodology and Billing Analysis Results Appendix V HEA Algori...
AI summary The document contains appendices related to the Home Energy Assessment (HEA), including survey questionnaires, results, tracking sheets, methodology, free-ridership calculations, spillover analysis, reporting requirements, and 2023 recommendations.
FREE-RIDERSHIP SERIES PROGRAMMING Upgrades Installed Programming Solar PV installed IF B4i=1 "Yes, installed", OR IF B1i=1 "Yes, recommended AND (B2=2 "Installed all upgrades" OR B3=1 "Yes, installed the upgrade") FOR SOLAR PV) Heat Pump i...
AI summary The document outlines programming logic for identifying free-ridership in energy efficiency upgrades, specifically for solar PV, heat pumps, and other energy efficiency upgrades, based on installation and recommendation criteria.
C. Free-ridership – Solar PV
AI summary This section discusses free-ridership related to Solar PV, focusing on how some customers may benefit from solar energy initiatives without contributing to the associated costs.
Factor [READ AND RANDOMIZE] Responses a. The program rebate Response 98 Don't Know Refused b. Information provided by Efficiency Nova Scotia on the benefits of high efficiency heat pumps Response 98 Don't Know Refused c. Information provid...
AI summary The text discusses responses to factors related to a program rebate and information on the benefits of high efficiency heat pumps, with most respondents indicating they don't know or refusing to answer. It also references free-ridership related to multiple energy efficiency upgrades.
UPGRADES INSTALLED. [AMONG FULL-BASE] 2023 Sample Size 100 Solar PV 29% Heat Pump 41% Other EE Upgrades 63% No Upgrades 6% C. Free-ridership – Solar PV
AI summary The data shows that in 2023, 29% of the sample size had solar PV installed, 41% had heat pumps, and 63% had other energy efficiency upgrades, with only 6% having no upgrades. The section discusses free-ridership related to solar PV installations.
D. Free-ridership – HP and No Other EE Upgrades D1/D2. Had you already decided to install a heat pump before you had your home evaluated by an energy advisor?
AI summary This section of the proceeding addresses free-ridership in the context of heat pump installations and the absence of other energy efficiency upgrades. It inquires whether individuals had already decided to install a heat pump prior to having their homes evaluated by an energy advisor.
E. Free-ridership – Other/Multiple EE Upgrades E1/E2. Did you already have plans to install the energy efficient upgrades that were installed through the program, BEFORE having your home evaluated by an energy advisor? Did you … Just to co...
AI summary The text asks whether the respondent had already planned to install energy efficiency upgrades before an energy advisor evaluated their home, seeking confirmation on the timing of their decisions.
Table 1: Free-Ridership Algorithm for Solar PV C3. If you had not received the rebate from Efficiency Nova Scotia, would you have paid the full cost of your solar PVsystem? (Scale 0 to 10) C3 = Answer x 10% IF DK OR REF: C3 = EMPTY C4. If...
AI summary The document outlines a free-ridership algorithm used to assess whether participants in the Solar PV program would have installed the system without the rebate. It includes questions and scoring mechanisms to evaluate the influence of the rebate and previous participation in Efficiency Nova Scotia programs.
Table 2: Free-Ridership Algorithm for Heat Pump and No Other Energy-efficient Upgrades Series A: Questions on the decision to install a heat pump D1. Had you already decided to install a heat pump before you had your home evaluated by an e...
AI summary This table outlines a free-ridership algorithm used to assess whether participants in a heat pump program would have installed a heat pump without the program's incentives. It includes a series of questions to determine the likelihood of installation and the impact of the program on the decision-making process.
C. Free-Ridership – Mini-split Heat Pumps
AI summary This section addresses free-ridership concerns related to mini-split heat pumps, highlighting potential issues where some customers may benefit from programs without contributing to their costs.
C10. I would like you to rate the influence of the following three factors in your decision to install a highefficiency heat pump. Use a scale from 0 to 10 where 0 is "No influence" and 10 is "Great influence". The first factor is… [READ A...
AI summary The text presents a survey question asking respondents to rate the influence of three factors on their decision to install a high-efficiency heat pump. The factors include program rebates and information provided by Efficiency Nova Scotia and retailers. The survey also transitions to a section on free-ridership related to biomass.
Factor [READ AND RANDOMIZE] Responses a. The program rebate Response 98 Don't Know Refused b. Information provided by Efficiency Nova Scotia Response 98 Don't Know Refused E. Free-ridership – Demand Measures
AI summary The document discusses free-ridership related to demand measures in the context of a regulatory proceeding. It includes a table with responses to questions about program rebates and information provided by Efficiency Nova Scotia, with a significant number of 'Don't Know' and 'Refused' responses.
Table 1: Free-Ridership Algorithm for Biomass D3. If you had not received the rebate from Efficiency Nova Scotia, would you have paid the entire cost of your ? (Scale 0 to 10) D3 = Answer x 10% IF DK OR REF: D3 = EMPTY D4. If there was no...
AI summary This table outlines a free-ridership algorithm used to assess the impact of the Green Heat program on biomass equipment installation. It includes questions to evaluate customer behavior, such as whether they would have installed equipment without the program, and calculates scores based on responses.
3.3.1 Free-ridership Free-ridership occurs when participant homeowners or participant builders would have implemented energy efficiency measures in the absence of the program component. The assessment of the NHC free-ridership level is bas...
AI summary Free-ridership in the NHC program is assessed through self-reported surveys of participant homeowners and builders. The 2023 free-ridership level was calculated using data from the 2019 evaluation and the proportion of NHC applicants in 2023. The overall free-ridership level for homeowners and builders combined was 24%.
Table 13: 2023 NHC NTGR Free-ridership Participant Spillover NTGR 24% 14% 0.90 3.3.4 Evaluated Net Savings
AI summary The document presents Table 13, which outlines the 2023 NHC NTGR with free-ridership at 24%, participant spillover at 14%, and a net-to-gross ratio of 0.90. It also references the section on 'Evaluated Net Savings' in the proceeding.
3.3.1 Free-ridership In the case of Application Rebates, free-ridership occurs when participants would have still implemented energy efficiency measures in the absence of the service. The free-ridership level was assessed during the 2021 e...
AI summary The free-ridership level for Application Rebates was assessed in 2021 using a self-report approach through telephone surveys. Since no new data collection was conducted in 2023, the 2021 level was used for the 2023 evaluation, as outlined in Table 11.
Table 11: 2023 Application Rebates Free-ridership Measure Category Average Free-ridership Level Margin of Error All 26% 6.6% 3.3.2 Spillover
AI summary The document presents Table 11, which shows the free-ridership level for the 2023 Application Rebates at 26% with a margin of error of 6.6%. The section '3.3.2 Spillover' suggests a discussion on the broader impacts or effects related to the rebate program.
3.3.3 Net-to-gross Ratio Calculation The NTGR is calculated using the following equation. NTGR = (1 – % Free-ridership + % Spillover) Using the free-ridership and spillover levels established for Application Rebates, the Evaluator calculat...
AI summary The document discusses the calculation of the Net-to-gross Ratio (NTGR) using the formula NTGR = (1 – % Free-ridership + % Spillover). The calculation is based on free-ridership and spillover levels established for Application Rebates, with the result presented in Table 12.
Table 12: 2023 Application Rebates NTGR Measure Category Free-ridership Spillover NTGR All 26% 0% 0.74 3.3.4 Evaluated Net Savings
AI summary The text presents Table 12, which outlines the 2023 Application Rebates NTGR, including free-ridership, spillover, and NTGR percentages. It also references Section 3.3.4, which evaluates net savings.
4.3.1 Free-ridership For Instant Rebates, free-ridership corresponds to the proportion of savings attributed to natural market trends. Thus, the free-ridership assessment is aimed at estimating the level of sales of energy efficient measur...
AI summary Free-ridership for Instant Rebates is assessed as the proportion of energy savings due to natural market trends rather than program influence. In 2022, a self-report approach using telephone surveys and interviews was used to evaluate free-ridership for three main lighting measures. The 2022 levels were carried forward in 2023 due to no new data collection.
Table 18: 2023 Instant Rebates Free-ridership Measure Category Average Distributor Influence Level (from distributor interviews) Free-ridership Level (from participant survey) Overall Free-ridership Level Margin of Error LED Linear Fixture...
AI summary Table 18 presents free-ridership levels for instant rebates in 2023, showing the influence of distributors and participant survey results for LED lighting measures. The data indicates varying degrees of free-ridership across different categories, with margin of error provided for each.
The NTGR is calculated using the following equation: NTGR = (1 – % Free-ridership) 13 At the time of writing, 2023 data were not yet available. The Nova Scotia-specific factor was obtained from Nova Scotia Power's 2022 total system emissio...
AI summary The document explains the calculation of the Net to Gross Evaluation (NTGR) using the formula NTGR = (1 – % Free-ridership). It references Nova Scotia Power's 2022 emissions data and electricity generation figures from multiple sources to establish NTGRs for different measure categories.
Custom General Key Findings and Recommendations 2023 Custom Finding: Custom achieved 21.746 GWh in net electrical energy savings and 3.053 MW in net peak demand savings at the generator in 2023, thus falling short of the planned electrical...
AI summary In 2023, Custom fell short of its energy and peak demand savings targets, partly due to decreased participation and adjustments made during project reviews. Free-ridership levels increased for Retrofit and solar PV projects, while spillover effects were minimal. Evaluated savings were lower than those tracked by EOne.
New Construction Key Findings and Recommendations 2023 New Construction Finding: Project reviews revealed continued room for improvement in the model review process. 2023 New Construction Finding: Free-ridership continued to remain relativ...
AI summary The 2023 New Construction findings indicate that the model review process still needs improvement and that free-ridership remains high at 31%, with factors outside New Construction influencing the construction of energy-efficient buildings.
Table 5: Implementation Status of Past Recommendations for Custom # Recommendations Status Comments 2022 New Construction R3 Monitor free-ridership for this service and undertake a process evaluation in 2023 or 2024 to provide recommendati...
AI summary Table 5 outlines the implementation status of past recommendations for the Custom New Construction R3 program. A free-ridership evaluation was conducted in 2023, revealing a 31% free-ridership level and market changes. A process evaluation is planned for 2024 to address the evolving market and regulatory context.
Table 6: 2022 Custom Evaluation Approach Evaluation Objectives Research Questions Methodology Calculate net results › What are the free-ridership and spillover levels for regular Retrofit, solar PV, and New Construction projects? › What ar...
AI summary The document outlines a 2022 Custom Evaluation Approach aimed at calculating net results through participant interviews and data analysis, focusing on free-ridership, spillover effects, energy savings, and GHG emissions reduction for various programs including Retrofit, solar PV, and New Construction.
Savings Verification In the fall of 2023 and January 2024, the Evaluator completed savings verifications of eight compressed air leak audit projects, by validating that the correct input parameters were used to estimate savings for all com...
AI summary In 2023 and early 2024, the Evaluator performed savings verification for eight compressed air leak audit projects, ensuring accurate input parameters were used. Additionally, interviews with 15 solar PV participants were conducted to assess free-ridership levels.
4.3.1 Free-ridership In the case of Retrofit, free-ridership occurs when participants would have still implemented energy efficiency upgrades and measures in the absence of the service. For compressed air leak audit projects, the NTGR was...
AI summary The document discusses the free-ridership assessment for Retrofit and solar PV projects, explaining how participants might have implemented energy efficiency measures independently of the program. The 2021 NTGR was used for compressed air leak audits due to missing 2022 and 2023 data. Free-ridership levels were assessed using self-reporting via phone interviews, with adjustments made based on participant influence from EOne programs. The evaluation used a sample size of 13 out of 20, and results were calculated using an algorithm in Appendix V.
Table 17: 2023 Retrofit Free-ridership Level per Project Category Retrofit Project Category Free-ridership Level Margin of Error Regular Retrofit 25% 6.3% Solar PV 38% 9.5% Compressed Air Leak Audit 16% 10.6% \ Free-ridership for compresse...
AI summary Table 17 presents the 2023 free-ridership levels for various retrofit project categories, including Regular Retrofit, Solar PV, and Compressed Air Leak Audit. The free-ridership level for Compressed Air Leak Audit is based on the 2021 evaluation due to lack of 2023 data.
4.3.3 Net-to-gross Ratio Calculation The NTGR was calculated using the following equation: NTGR = (1 – % Free-ridership + % Participant Spillover) [Table](#page-25-1) 18 outlines the free-ridership and spillover levels established for Retr...
AI summary The Net-to-gross Ratio (NTGR) is calculated using the equation NTGR = (1 – % Free-ridership + % Participant Spillover). Table 18 outlines free-ridership and spillover levels for Retrofit, along with corresponding NTGR values for each project category.
Table 18: Evaluated 2023 Retrofit NTGRs Project Category Free-ridership Level Spillover Level NTGR Regular Retrofit 25% 2% 0.77 Solar PV 38% 1% 0.63 Compressed Air Leak Audit 16% 0% 0.84 \ Free-ridership and spillover for compressed air le...
AI summary Table 18 presents the evaluated 2023 Retrofit NTGRs for different project categories, including free-ridership and spillover levels. Values for compressed air leak audit projects are from the 2021 evaluation as they were not measured in 2023.
5.3.1 Free-ridership In the case of New Construction, free-ridership occurs when participants would have still implemented energy efficiency measures in their new building in the absence of the service. The free-ridership level was assesse...
AI summary This section discusses the assessment of free-ridership in the New Construction program of EOne. The evaluation used a self-report approach via phone interviews and found a 31% free-ridership level, a decrease from 39% in 2022. The results indicate that factors outside the program, such as other funding sources, had a larger influence on decision-making.
5.3.2 Net-to-gross Ratio Calculation The NTGR is calculated using the following equation: $$NTGR = (1 - \% Free-ridership)$$ [Table](#page-33-0) 23 outlines the free-ridership level established for New Construction along with the NTGR valu...
AI summary The net-to-gross ratio (NTGR) is calculated as 1 minus the percentage of free-ridership. The free-ridership level for New Construction is outlined in Table 23, along with the resulting NTGR value. Participant spillover is not included in the calculation, as it was assumed to be zero.
Table 23: Evaluated 2023 New Construction NTGR Projects Fully Claimed in 2023 Free-ridership Level 31% NTGR 0.69 5.3.3 Evaluated Net Savings
AI summary Table 23 presents the evaluated 2023 New Construction NTGR with a free-ridership level of 31% and an NTGR value of 0.69. Section 5.3.3 discusses the evaluated net savings.
Gross Savings NTGR Net Savings Realization Rate Value Unit Value Value Unit Value Energy Savings Tracked Savings by EOne 7.937 GWh 0.61 4.842 GWh Evaluation Results 6.581 GWh 0.69 4.529 GWh 94% Peak Demand Savings Tracked Savings by EOne 2...
AI summary Evaluated energy and peak demand savings were lower than those tracked by EOne due to adjustments in energy models, leading to a downward adjustment in gross savings. This was partially offset by a lower free-ridership level compared to the tracked value.
6.3.1 Net-to-gross Ratio Calculation The NTGR was calculated using the following equation: NTGR = (1 – % Free-ridership + % Participant Spillover) [Table](#page-38-1) 28 outlines the free-ridership and spillover levels established for Buil...
AI summary The Net-to-gross Ratio (NTGR) is calculated using the formula (1 – % Free-ridership + % Participant Spillover). Table 28 provides the free-ridership and spillover levels for Building Optimization and the resulting NTGR value.
Table 28: Evaluated 2023 Building Optimization NTGR Value for Savings Claimed in 2023 Free-ridership Level 9% (±7.2%) Participant Spillover Level 0% NTGR 0.91 Free-ridership and spillover levels were not measured in 2022 or 2023, results f...
AI summary Table 28 evaluates the 2023 Building Optimization NTGR, showing a free-ridership level of 9% (±7.2%) and a spillover level of 0%. The NTGR value is reported as 0.91, with data from the 2021 Custom evaluation used due to the absence of measurements in 2022 and 2023.
ship was not measured for Building Optimization and was therefore maintained at 9% (value from 2021). The 2023 free-ridership level for New Construction was established at 31%, down from 39% in 2022. Retrofit spillover was established at 2...
AI summary The 2023 free-ridership level for New Construction was reduced to 31% from 39% in 2022. Retrofit spillover rates were set at 2% for regular Retrofit projects and 1% for solar PV projects. However, the evaluated net energy and peak demand savings were found to be 3% and 9% lower than those tracked by EOne, due to adjustments made during project reviews.
New Construction Key Findings and Recommendations 2023 New Construction Finding: Project reviews revealed continued room for improvement in the model review process. The reviews of New Construction projects revealed that the building energ...
AI summary The 2023 New Construction evaluation found that while building energy models aligned with design specs, site visits revealed less efficient equipment was installed. Free-ridership remained high at 31%, with participants citing external factors as motivators for energy efficiency. EOne plans a process evaluation for 2024.
Custom Appendix I Retrofit and SEM Interview Guide with BDMS Appendix II Retrofit and Solar Participant Interview Guide Appendix III Retrofit Consultant Interview Guide Appendix IV Custom Tracking Sheet Audit Appendix V Retrofit Algorithm...
AI summary The document outlines various appendices related to energy efficiency programs, including interview guides, tracking sheets, algorithms for free-ridership and spillover calculations, project review protocols, and recommendations for a custom 2023 initiative.
C. Free-ridership [Retrofit / Solar] - C1. Had your organization finalized the details of the [solar PV / energy efficiency] project BEFORE knowing that you would receive an incentive from Efficiency Nova Scotia? - 1. Yes - 2. No - 98. Don...
AI summary This section of the regulatory proceeding explores free-ridership related to solar PV and energy efficiency projects, focusing on whether organizations finalized project details before knowing about incentives from Efficiency Nova Scotia, their confidence in receiving incentives, and the impact of these incentives on project payback periods and likelihood of implementation.
APPENDIX V Retrofit Algorithm for Free-Ridership Calculation Question Response Score Identifying Key Decision-makers A1a. We would like to speak with someone that played a key role in the financial decision to implement the energy efficien...
AI summary This section of the document outlines an appendix titled 'Retrofit Algorithm for Free-Ridership Calculation' and includes a table with a question about identifying key decision-makers involved in energy efficiency projects. The table includes response options and a score column.
C. Free-ridership C1. [CAPTURE VERBATIM; KEEP CODE 98 & 96 EXCLUSIVE] Why did your organization decide to build a better-than-code building? CAPTURE VERBATIM - 98. I am unsure - 96. I prefer not to say - C2. [SINGLE RESPONSE] Was the desig...
AI summary The text outlines a section on free-ridership in a regulatory proceeding, including questions about the design of better-than-code buildings and whether incentives from Efficiency Nova Scotia influenced the decision. It includes response options and a conditional question based on previous answers.
B. Free-ridership - B1. Why did your organization decide to build a better-than-code building? [DO NOT READ] - 1. Lower operation costs - 2. Energy policy in my organization - 3. Environmental reasons/energy efficiency - 4. Competitors are...
AI summary The text outlines a series of questions related to free-ridership in energy efficiency programs, focusing on how financial incentives from Efficiency Nova Scotia influenced building design and energy efficiency measures. It explores whether incentives were a key factor in decision-making and whether they encouraged exceeding building code standards.
Table 1: Participant Interview Questionnaire and Free-ridership Algorithm Question (From the Custom New Construction Participant Interview Guide) Response Score Question (From the Custom New Construction Participant Interview Guide) Respon...
AI summary This document presents a table titled 'Participant Interview Questionnaire and Free-ridership Algorithm' with columns for questions, responses, and scores. The table is associated with the Custom New Construction Participant Interview Guide and is likely used to assess free-ridership in energy efficiency programs.
B. Free-ridership and Spillover Series of questions to be asked for each measure. - B1. Without the support from EPS, would you have identified this energy efficiency opportunity? - 1. Yes - 2. No - 98. Don't know - 99. Refused - B2. [Ask...
AI summary This section outlines a series of questions to assess free-ridership and spillover effects related to energy efficiency projects. It asks whether support from EPS and Efficiency Nova Scotia influenced the identification and completion of these projects.
Interviewed participants were asked free-ridership validation questions. These questions are listed at the end of the protocol. - € Efficiency Nova Scotia Validation of the measure (is it installed and operational?) Savings calculation app...
AI summary The text outlines free-ridership validation questions asked of interviewed participants, focusing on the verification of energy efficiency measures, savings calculation approaches, baseline appropriateness, and statistical analysis compliance with standards such as IPMVP.
Table 5: 2023 SBES Evaluation Approach Evaluation Objectives Research Questions Methodology Collect information on participant, partner, and staff perspectives › What is the level of satisfaction with the service among participants and con...
AI summary Table 5 outlines the 2023 SBES Evaluation Approach, which includes collecting participant and contractor perspectives, calculating gross and net results, and evaluating program effectiveness. It involves various methods such as interviews, surveys, tracking sheet audits, and site visits.
4.3.1 Free-ridership Free-ridership occurs when participants would have implemented energy efficiency upgrades in the absence of the program component. The free-ridership levels for DIY and Audit projects were last assessed during the 2021...
AI summary Free-ridership levels for energy efficiency programs were reassessed in 2023. The DIY path had a free-ridership level of 20%, up from 15% in 2021 and 7% in 2019. Lighting participants showed a significant increase in pre-planned high-efficiency lighting installations. The Audit path retained its 2019 level of 12% due to limited data. The CDI pilot assumed no free-ridership.
Table 21: 2023 SBES Free-ridership Levels Project Path Average Free-ridership Level Margin of Error DIY 20% 5.2% Audit (based on 2019 results) 12% 6.5% CDI Pilot - N/A
AI summary Table 21 presents the 2023 free-ridership levels for the Small Business Energy Solutions (SBES) program, showing varying levels across different project paths, including DIY, Audit, and CDI Pilot, with associated margin of error values.
4.3.3 Net-to-gross Ratio Calculation The NTGR for SBES was calculated using the following equation. NTGR = (1 – % Free-ridership + % Participant Spillover) Based on the established DIY and Audit path free-ridership levels, the NTGRs were e...
AI summary The Net-to-Gross Ratio (NTGR) for the SBES program was calculated using the formula (1 – % Free-ridership + % Participant Spillover). The NTGRs were estimated at 0.88 for the Audit path and 0.80 for the DIY path, with the CDI pilot assumed to have an NTGR of 1.00.
5 SBES Key Findings and Recommendations The main objectives of the 2023 SBES evaluation were as follows: - › Collect information on participant and partner perspectives - › Calculate gross and net SBES results, namely electrical first-year...
AI summary The 2023 SBES evaluation found that net energy and peak demand savings fell short of targets by 37% and 28%, respectively. Participation decreased by 27%, and free-ridership for DIY projects increased to 20%. The Evaluator made minor adjustments to savings calculations and recommended improving the CIS data entry process. Participant satisfaction remained high.
C. Free-Ridership for Audit Path [IF PROJECT TYPE IS AUDIT IN SAMPLE, ASK [C1](#page-17-0) TO [C9;](#page-19-0) OTHERWISE SKIP TO FREE-RIDERSHIP FOR DIY PATH (SECTION [D)](#page-19-1)] - C1. BEFORE learning about Small Business Energy Solu...
AI summary This section of the regulatory proceeding document asks questions about free-ridership in the context of the Small Business Energy Solutions Program. It explores whether businesses would have installed energy-efficient upgrades without the rebate or audit, and how likely they would have paid for the upgrades without financial assistance.
Factor (READ AND RANDOMIZE) Response a. The free audit Response98 Don't Know99 Refused b. Information or advice from the small business energy auditor Response98 Don't Know99 Refused c. [IF TOTAL ENS REBATE>0] The program rebate Response98...
AI summary This section discusses free-ridership related to DIY path options for lighting and HVAC measures. It includes responses to various factors such as free audits, information from energy auditors, program rebates, on-bill financing, and contractor information. Most responses are 'Don't Know' or 'Refused.'
D. Free-ridership for DIY (Lighting and HVAC measures) D1. Had your business already decided to install the energy-efficient lighting upgrades BEFORE learning about the Small Business Energy Solutions Program?
AI summary This section addresses whether a business had already decided to install energy-efficient lighting upgrades before learning about the Small Business Energy Solutions Program, focusing on free-ridership for DIY measures in lighting and HVAC.
APPENDIX VIII SBES: Free-Ridership Algorithm The figures below present the algorithms for calculating the SBES free-ridership levels for the Audit path and DIY path (lighting and HVAC measures). The participant survey questionnaire include...
AI summary This appendix details the algorithm used to calculate free-ridership levels for the SBES program, specifically for the Audit and DIY paths. The algorithm is based on survey data assessing various aspects of program components, which are then used to determine the level of free-ridership per participant.
Table 1: FR – Audit Path C3. If your business had not received the free audit of your facility by a certified energy auditor, would you have identified the opportunity for the energy efficient upgrades that you installed? IF 1. Yes: EMPTY...
AI summary The audit path table examines whether businesses would have identified energy efficiency opportunities without the SBES program and includes inconsistency tests and free-rider calculations to assess program effectiveness and participation impact.