- Build pipeline of standardized decarbonization projects
Provide Momentum tool for analysis and recommendations to Aurora / First Service
Handle data integrity process
Generate and share compliance scopes
Initiate and manage bidding processes
Receive 3% transaction fee |
| Aurora Energy | - Handle benchmarking and energy data
Serve as energy compliance trusted advisor
Assist with data collection
Assist with program marketing
Receive 3% transaction fee |
| First Service Energy | - Similar role to Aurora Energy for First Service Residential portfolio
Assist with data collection
Assist with program marketing
Receive 3% transaction fee |
| Third-party Property Managers
(e.g., Akam, Elliman, Century, Orsid, Halstead) | - Share time and bandwidth with the program
Provide building data and access
Verify basic building data characteristics
Potentially receive a portion of transaction fees (via Aurora) |
| Building Owners | - Provide necessary building information
Make decisions on recommended actions
Fund and implement approved projects |
| Other Service Providers
(e.g., ReDocs, BrightPower) | - Potential future partners with similar roles to Aurora/First Service Energy
Would not share fees with real estate portfolios |
| Contractors | - Bid on and execute optimization and compliance projects |
Real estate likes to pay for projects at transactions -- in the near term, particularly with third party managers, I believe that there is more value we can get out of real estate paying us in terms of their time, organizational buy-in and data than in up-front subscriptions.
Aurora energy handles the benchmarking, energy data and serves as an energy compliance trusted advisor for 1,500 buildings across most of the larger 3rd party managers (including Akam, Elliman, Century, Orsid, Halstead, etc). First Service Energy plays a role similar role as a subsidiary of First Service Residential, the single largest manager (1000 buildings in NYC alone). ReDocs, BrightPower and other service providers also have their own real estate customer portfolios that they serve. All these service providers are gatekeepers and want to get paid without getting their hands too dirty.
It is also true that there are very few pragmatic pathways to achieving 2030 LL97 compliance for the vast majority of multifamily buildings. When it comes to moving the carbon compliance needle, it kind of comes down to steam/hydronic heating optimization, partial space heating electrification and partial DHW electrification -- and maybe oil to gas conversion. There is therefore an opportunity to build a pipeline of these standardized decarb project at scale and collect transaction fees.
BLUF: I'd like to focus on building a pipeline of 1,000 standardized decarb projects and capture the for market rate compliance sector instead of continuing to push on the string of subscriptions (at least in the near term). I think I could get organizational sign off on this by end of September for real estate organizations representing 1000-1500 buildings and get Momentum outputs baked into their fall budget planning for 2025.
Rather than an initial subscription sale, for real estate portfolios that agree to share their time and bandwidth with us... I propose that we follow the process below in partnership with Aurora and First Service Energy -- Aurora and First Service Energy share the burden of data collection and program marketing -- and we split transactional revenues with them across the real estate portfolios that they support -- 3% for us and 3% for them. Aurora and First Service Energy are unique in that revenues that they generate are shared with the 3rd party manager (Aurora has a direct fee split with multiple managers; First Service Energy is a subsidiary of a manager). We then go to all the service providers (ReDocs, BrightPower, etc) and pitch them on a version of this model before year end for additional buildings in other real estate portfolios. ReDocs, BrightPower and other service providers don't share fees with the real estate portfolios that they work for, but that doesn't matter to us.
Above approach would not preclude us from going back to the 3rd party managers that we reach through Aurora and First Service Energy and hitting them up for subscriptions in 2025 for new feature sets such as portfolio view, etc. Same applies to BrightPower/ReDocs, etc. And at that point, all of these organizations would have organizational buy-in and built up transactional revenue streams that could internally fund/justify paying for additional subscription cost/features. NTM that on the transactional revenue side, whatever percentage that we start with is the floor. As we collect more transactional data and when we figure out true aggregated bidding and payments, we will be delivering more value and have more pricing power. Similarly, as we figure out how to add new scope types (especially ones that go beyond decarb) we will increase our LCV. Also, this model would be highly compatible with a world where we win the Retrofit Accelerator contract. And even if we don't win this award, this model would make us much more valuable to Willdan for the Con Ed recompete in 2025.
In my view... with real estate easier to get started with transactions and then expand to subscription then the other way around. NTM there is a limited window where the market is still wide open -- there are no switching costs since no one has picked a horse for this journey -- but as soon as particular buildings start down some road, it will be very resource intensive to acquire them as customers. On the third party manager side, only the two largest managers really have the potential appetite (and the scale) for eating a subscription fee at this point (Akam and First Service) -- but I feel like this is kind of a distraction especially since closing these sales will cause missing capital budget planning season. Yes of course, market rate owners can theoretically find money... but honestly these owners are a pain in the ass -- they tend to have a basic handle on things so a partial (i.e. subscription planning) solution alone isn't super interesting -- I am talking to Durst first week of September. I really don't think I am going to sell any of them this fall -- but I could get many of them interested in being part of our 2025 decarb bidding program (Moinian is already in with this kind of approach).
Oh and as far as fairness... note that Related's subscription involves bidding features whenever we have ready -- if they do a ton of construction in 2025 and 2026, it is a better deal for them to commit to this ongoing subscription then to pay purely transactionally... although post 2026 we would seek to convert them to a subscription + transaction fee arrangement. And Buchbinder's subscription involves my consulting time -- and is a portfolio that I'd like to convert to subscription + transaction model in 2025.
💬 1 comment
Bomee Jung, Aug 12, 2024: I need clarity on who is paying what.
This is the process I personally follow to make our tool valuable to market rate building owners and managers. While not rocket science, pure real estate end users cannot / will not use do these steps on their own -- making simple access to Momentum in its current form not anywhere near as valuable as it could be. And it should go without saying, but if the owners goal is to get to eliminate carbon fines, data integrity associated with those fines is critical to defining capital planning actions -- and therefore makes the engagement of the Aurora's and First Service Energy's of the world helpful -- but the are not going to get engaged unless they can make money.
Step 1: Basic building data review. Number of units, number of stories & year built can all be wrong on occasion. These impact credibility and cost estimating.
Backwards looking example: In looking at ~20 Related buildings, the number of units was "1" for one building... I corrected based on streeteasy. And year built was about 12 years off for another building (the difference between 2018 and 2006 is meaningful for a developer and in terms of systems -- the difference between 1920 and 1908 is not terribly meaningful). I have seen funky things in past like "2" stories for a building that was much, much taller... though don't recall seeing this in a while. And while I personally don't care about difference in 12 vs 13 stories (a co-op board member seeing an output report might since that is one of the few pieces of information about the building that they themselves know)
Potential forward looking approach: Service provider asks property managers to verify basic building data characteristics. Frankly, the easiest way to do this would be for us to export a list of a few columns to managers to verify (rather than having dozens of non power user and busy with other stuff property managers find the handful of buildings in momentum that they manage and click into each building)
💬 1 comment
Bomee Jung, Aug 12, 2024: I don't understand dthis
Aside: Going back to property managers with questions about basic building systems is a good opportunity to also ask them the following questions with some typeform: (1) do you have plans to replace boilers or roofs or windows in next 5 years; (2) do you have plans to refi in next 5 years; (3) have you already done LEDs in your corridors; (4) have you already installed an Energy Management System with wireless sensors in some apartments; (5) is building SF from a measured survey? (6) does your building have setbacks?
Step 2: Systems data review. Primarily applies to post ~1980 market rate buildings where there is more diversity in potential system types.
Backwards looking example: Sometimes Momentum misses if a cooling tower is present (google street view); sometimes "steam PTAC" is a better system choice than the default that I often see of "2-PS"
Potential forward looking approach: MZ can just look at the buildings in google, zero-ing in on (the relatively small fraction of) post ~1980 buildings if we had a Momentum output report with year built and assumed HVAC system type. Doesn't take too long.. perhaps this will be done by image recognition.... but not a big deal for me to do at this point. Also, First Service Residential has already gone through this exercise and has pretty good data on it for their ~1000 NYC buildings.
Step 3: Square footage review
Backwards looking example... for a Buchbinder building, reported SF was 2x larger than likely actual SF (based on google maps take-off) for one of their buildings. For many (but not all) older buildings, SF/DU tends to fall in somewhat tight band making outliers possible to spot. For newer market rate buildings, this can be harder to spot given tenant space impact.
Forward looking approach: A momentum csv output with year built and SF / DU along with residential vs. other tenant space break out by type and SF and would be helpful to look at systematically in order to spot outliers. For buildings without setbacks, a column with an automated Momentum check on SF that uses shape files would be amazing (because we can't yet do this for buildings with setbacks, correct Robin?)
Step 4: Utility data review
Backwards looking example: A few Related buildings have very high fossil fuel use relative to comps indicating potential cogen installation. Data quality errors (including wrong SF) can also contribute to this metric being off. I focus on this metric because it drives carbon footprint and fines - and is most correlated with decarbonization infrastructure changes.
Forward looking approach: A Momentum csv output with FF use intensity electricity use intensity and carbon fine per SF would help spot outliers.
Turning data integrity into Calls to Action (CTAs) #
Based on the above, any building in a portfolio should probably fall into one of these buckets
Bucket 0: Custom
CTA: (1) Data is still funky and some human being needs to look at → could be benchmarking provider/Aurora/First Service OR (2) it is a snowflake building with a high fine that is probably best suited for a targeted feasibility study. For instance, fuel fired chiller buildings should probably get an engineering feasibility study to go from fossil fuel to electric chillers -- we could ultimately define scope and bid out such feasibility studies.... but not a top priority given small number of buildings
Bucket 1: No 2030 fine
CTA: Probably nothing. Alternatively, for buildings that are relatively close to the fine threshold, they may want to consider Bucket 2 CTA steps as cheap insurance.
Bucket 2: Relatively lower 2030 fine (i.e. optimize of steam or hydronic distribution alone will get them under the 2030 fine threshold)
CTA:
(1) Share Momentum "Optimize" scope with decision maker (PDF output is the easiest thing to share but if someone wants to spend the time typing in each of the board members personal emails for every building a custom scope link could also be shared -- at which point, they will ask for a PDF anyway). Having a one pager piece of collateral and explainer video for steam vs hydronic video would be helpful. Why now: building optimization is the low hanging fruit but won't know for sure how low you can go until you put system in and operate -- important to establish a new baseline now to confirm you can operate under the 2030 limit with out additional major capital investment.
(2) If building owner wants to proceed, initiate generic project bidding process for optimize scope (which is basically a version of the Article 321 scope)
(3) Also bid out building SF measurement since these buildings are going to be the ones most on the bubble
Bucket 3: Relatively higher fine (something more than steam or hydronic optimization required to get them under the 2030 fine threshold)
CTA:
(1) Share Momentum BE 2030 compliance scopes with decision maker and alternate (much more painful) compliance scope that doesn't use the BE credit. Having a one pager piece of collateral and explainer vide for steam vs hydronic video would be helpful. Why now: waiting will cost more and be riskier.
(2) If building owner wants to proceed, initiate generic project bidding process for Optimize scope (if applicable)
(3) If building owner wants to proceed, initiate generic project bidding process for BE credit feasibility scope that would proceed deeper investment:
~5k feasibility study for DHW heat pumps and/or
~ $10-20k PTHP pilot involving installing units in a handful of apartments to assess fit and user experience
(4) if building owner wants to proceed, based on outcomes of feasibility study, initiate generic project bidding process for full BE credit scope
(5) perhaps also bid out SF measurement
(note that I didn't figure out how to work oil to gas conversion into the above logic but this is already much too long)
Review and verify basic building data (units, stories, year built)
Review and verify building systems data, especially for post-1980 buildings
Review and verify square footage data
Review and analyze utility data for anomalies
Building Assessment:
Categorize buildings into appropriate "buckets" based on 2030 fine status
Identify buildings requiring custom solutions or targeted feasibility studies
Stakeholder Engagement:
Communicate with property managers to verify building data
Engage with decision-makers to share compliance scopes and recommendations
Explain the importance and urgency of taking action
Project Planning:
Develop standardized scopes for common decarbonization projects
Create explanatory materials (one-pagers, videos) for different optimization strategies
Initiate and manage generic project bidding processes
Strategic Planning:
Work with real estate organizations to integrate decarbonization plans into their fall budget planning for 2025
Develop partnerships with service providers and property managers
Market Analysis:
Identify opportunities for oil-to-gas conversion projects
Stay informed about market trends and emerging technologies in building decarbonization
Compliance Strategy:
Develop strategies for utilizing Building Electrification (BE) credits
Plan and oversee pilot projects (e.g., PTHP installations) to assess feasibility and user experience
Financial Analysis:
Analyze cost-effectiveness of various compliance strategies
Advise on financial implications of different decarbonization approaches
These tasks represent the non-software aspects of the consulting work required to implement the proposed decarbonization program effectively.
Automated data import from various sources (e.g., benchmarking databases, utility data)
Cross-referencing building data with public records for basic information verification
Flagging discrepancies or outliers in building data for human review
Building Systems Analysis:
Automated identification of likely HVAC systems based on building age and type
Integration with Google Street View API to detect visible systems (e.g., cooling towers)
Square Footage Calculation:
Automated calculation of building square footage using shape files for buildings without setbacks
Flagging buildings with potential square footage discrepancies based on units/square foot ratios
Utility Data Analysis:
Automated calculation of energy use intensities and carbon emissions
Identification of outliers in energy consumption patterns
Detection of potential cogeneration installations based on high fossil fuel use
Building Categorization:
Automated sorting of buildings into appropriate "buckets" based on 2030 fine status and other criteria
Flagging of buildings that may require custom solutions
Compliance Scope Generation:
Automated generation of compliance scopes based on building characteristics and fine status
Creation of standardized "Optimize" and "BE 2030 compliance" scopes
Financial Calculations:
Automated calculation of potential fines under various scenarios
Cost-benefit analysis of different compliance strategies
Report Generation:
Automated creation of PDF reports for decision makers
Generation of CSV outputs for further analysis
Project Bidding Process:
Automated initiation of generic project bidding processes based on building categorization
Matching of contractors to appropriate projects based on scope and expertise
Communication Automation:
Automated emails to property managers for data verification
Scheduled reminders for important deadlines or action items
Dashboard Creation:
Real-time visualization of portfolio compliance status
Tracking of project progress across multiple buildings
Machine Learning Applications:
Predictive modeling of energy use based on building characteristics and historical data
Optimization of compliance strategies based on cost and effectiveness across the portfolio
These automations can significantly streamline the process, reduce manual work, and improve accuracy in the NYC decarbonization program.
And for shits & giggles: Potential AI Contributions #
Data Analysis and Anomaly Detection:
Utilize machine learning algorithms to analyze large datasets of building information and energy usage
Identify anomalies or outliers in building data that may indicate errors or unique situations
Predict future energy consumption patterns based on historical data and building characteristics
Building System Identification:
Employ computer vision AI to analyze Google Street View images for visible building systems (e.g., cooling towers)
Use natural language processing (NLP) to extract relevant information from building documents and reports
Optimized Decarbonization Strategies:
Develop AI models to recommend optimal decarbonization strategies based on building characteristics, budget constraints, and compliance requirements
Continuously refine recommendations based on the outcomes of implemented projects
Predictive Maintenance:
Use AI to predict when building systems may need maintenance or replacement, helping to optimize the timing of decarbonization projects
Natural Language Generation for Reporting:
Generate human-readable reports and summaries of building status, recommended actions, and potential outcomes
Customize communication style based on the intended audience (e.g., property managers, building owners, or technical staff)
Chatbots for Stakeholder Engagement:
Implement AI-powered chatbots to answer common questions from property managers and building owners
Provide 24/7 support for basic inquiries about the program and compliance requirements
Project Cost Estimation:
Develop AI models to estimate project costs based on building characteristics, scope of work, and current market conditions
Refine estimates over time as more project data becomes available
Compliance Forecasting:
Use machine learning to forecast a building's likelihood of meeting compliance targets based on current status and planned improvements
Identify buildings at risk of non-compliance early in the process
Energy Modeling:
Employ AI to create more accurate and faster energy models of buildings, considering various upgrade scenarios
Intelligent Document Processing:
Use AI to extract relevant information from building plans, energy audits, and other technical documents
Automatically populate database fields with extracted information
Personalized Action Plans:
Generate tailored action plans for each building, considering its unique characteristics, budget constraints, and compliance status
Market Trend Analysis:
Analyze market data to predict trends in energy prices, technology costs, and regulatory changes that may impact decarbonization strategies
Contractor Matching:
Use AI to match buildings with the most suitable contractors based on project requirements, contractor expertise, and past performance
Optimization of Building Operations:
Implement AI-driven building management systems to optimize energy use in real-time, contributing to overall decarbonization efforts
Sentiment Analysis:
Analyze communications and feedback from stakeholders to gauge sentiment and identify potential issues or resistance to the program
Strategy Notebook
NYC Market Rate Decarb Pipeline Building Steps
Created: August 12, 2024
The NYC Article 320 Market Opportunity
Summary #
Problem and Opportunity #
Business Model #
Key Points #
Roles #
Problem to solve #
Make it as easy as possible to get market rate buildings into LL97 carbon compliance at low cost and low risk
Constraints & Resources #
Real estate likes to pay for projects at transactions -- in the near term, particularly with third party managers, I believe that there is more value we can get out of real estate paying us in terms of their time, organizational buy-in and data than in up-front subscriptions.
Aurora energy handles the benchmarking, energy data and serves as an energy compliance trusted advisor for 1,500 buildings across most of the larger 3rd party managers (including Akam, Elliman, Century, Orsid, Halstead, etc). First Service Energy plays a role similar role as a subsidiary of First Service Residential, the single largest manager (1000 buildings in NYC alone). ReDocs, BrightPower and other service providers also have their own real estate customer portfolios that they serve. All these service providers are gatekeepers and want to get paid without getting their hands too dirty.
It is also true that there are very few pragmatic pathways to achieving 2030 LL97 compliance for the vast majority of multifamily buildings. When it comes to moving the carbon compliance needle, it kind of comes down to steam/hydronic heating optimization, partial space heating electrification and partial DHW electrification -- and maybe oil to gas conversion. There is therefore an opportunity to build a pipeline of these standardized decarb project at scale and collect transaction fees.
Biz model #
BLUF: I'd like to focus on building a pipeline of 1,000 standardized decarb projects and capture the for market rate compliance sector instead of continuing to push on the string of subscriptions (at least in the near term). I think I could get organizational sign off on this by end of September for real estate organizations representing 1000-1500 buildings and get Momentum outputs baked into their fall budget planning for 2025.
Rather than an initial subscription sale, for real estate portfolios that agree to share their time and bandwidth with us... I propose that we follow the process below in partnership with Aurora and First Service Energy -- Aurora and First Service Energy share the burden of data collection and program marketing -- and we split transactional revenues with them across the real estate portfolios that they support -- 3% for us and 3% for them. Aurora and First Service Energy are unique in that revenues that they generate are shared with the 3rd party manager (Aurora has a direct fee split with multiple managers; First Service Energy is a subsidiary of a manager). We then go to all the service providers (ReDocs, BrightPower, etc) and pitch them on a version of this model before year end for additional buildings in other real estate portfolios. ReDocs, BrightPower and other service providers don't share fees with the real estate portfolios that they work for, but that doesn't matter to us.
Above approach would not preclude us from going back to the 3rd party managers that we reach through Aurora and First Service Energy and hitting them up for subscriptions in 2025 for new feature sets such as portfolio view, etc. Same applies to BrightPower/ReDocs, etc. And at that point, all of these organizations would have organizational buy-in and built up transactional revenue streams that could internally fund/justify paying for additional subscription cost/features. NTM that on the transactional revenue side, whatever percentage that we start with is the floor. As we collect more transactional data and when we figure out true aggregated bidding and payments, we will be delivering more value and have more pricing power. Similarly, as we figure out how to add new scope types (especially ones that go beyond decarb) we will increase our LCV. Also, this model would be highly compatible with a world where we win the Retrofit Accelerator contract. And even if we don't win this award, this model would make us much more valuable to Willdan for the Con Ed recompete in 2025.
In my view... with real estate easier to get started with transactions and then expand to subscription then the other way around. NTM there is a limited window where the market is still wide open -- there are no switching costs since no one has picked a horse for this journey -- but as soon as particular buildings start down some road, it will be very resource intensive to acquire them as customers. On the third party manager side, only the two largest managers really have the potential appetite (and the scale) for eating a subscription fee at this point (Akam and First Service) -- but I feel like this is kind of a distraction especially since closing these sales will cause missing capital budget planning season. Yes of course, market rate owners can theoretically find money... but honestly these owners are a pain in the ass -- they tend to have a basic handle on things so a partial (i.e. subscription planning) solution alone isn't super interesting -- I am talking to Durst first week of September. I really don't think I am going to sell any of them this fall -- but I could get many of them interested in being part of our 2025 decarb bidding program (Moinian is already in with this kind of approach).
Oh and as far as fairness... note that Related's subscription involves bidding features whenever we have ready -- if they do a ton of construction in 2025 and 2026, it is a better deal for them to commit to this ongoing subscription then to pay purely transactionally... although post 2026 we would seek to convert them to a subscription + transaction fee arrangement. And Buchbinder's subscription involves my consulting time -- and is a portfolio that I'd like to convert to subscription + transaction model in 2025.
💬 1 comment
My data integrity process starting point #
This is the process I personally follow to make our tool valuable to market rate building owners and managers. While not rocket science, pure real estate end users cannot / will not use do these steps on their own -- making simple access to Momentum in its current form not anywhere near as valuable as it could be. And it should go without saying, but if the owners goal is to get to eliminate carbon fines, data integrity associated with those fines is critical to defining capital planning actions -- and therefore makes the engagement of the Aurora's and First Service Energy's of the world helpful -- but the are not going to get engaged unless they can make money.
Step 1: Basic building data review. Number of units, number of stories & year built can all be wrong on occasion. These impact credibility and cost estimating.
Backwards looking example: In looking at ~20 Related buildings, the number of units was "1" for one building... I corrected based on streeteasy. And year built was about 12 years off for another building (the difference between 2018 and 2006 is meaningful for a developer and in terms of systems -- the difference between 1920 and 1908 is not terribly meaningful). I have seen funky things in past like "2" stories for a building that was much, much taller... though don't recall seeing this in a while. And while I personally don't care about difference in 12 vs 13 stories (a co-op board member seeing an output report might since that is one of the few pieces of information about the building that they themselves know)
Potential forward looking approach: Service provider asks property managers to verify basic building data characteristics. Frankly, the easiest way to do this would be for us to export a list of a few columns to managers to verify (rather than having dozens of non power user and busy with other stuff property managers find the handful of buildings in momentum that they manage and click into each building)
💬 1 comment
Aside: Going back to property managers with questions about basic building systems is a good opportunity to also ask them the following questions with some typeform: (1) do you have plans to replace boilers or roofs or windows in next 5 years; (2) do you have plans to refi in next 5 years; (3) have you already done LEDs in your corridors; (4) have you already installed an Energy Management System with wireless sensors in some apartments; (5) is building SF from a measured survey? (6) does your building have setbacks?
Step 2: Systems data review. Primarily applies to post ~1980 market rate buildings where there is more diversity in potential system types.
Backwards looking example: Sometimes Momentum misses if a cooling tower is present (google street view); sometimes "steam PTAC" is a better system choice than the default that I often see of "2-PS"
Potential forward looking approach: MZ can just look at the buildings in google, zero-ing in on (the relatively small fraction of) post ~1980 buildings if we had a Momentum output report with year built and assumed HVAC system type. Doesn't take too long.. perhaps this will be done by image recognition.... but not a big deal for me to do at this point. Also, First Service Residential has already gone through this exercise and has pretty good data on it for their ~1000 NYC buildings.
Step 3: Square footage review
Backwards looking example... for a Buchbinder building, reported SF was 2x larger than likely actual SF (based on google maps take-off) for one of their buildings. For many (but not all) older buildings, SF/DU tends to fall in somewhat tight band making outliers possible to spot. For newer market rate buildings, this can be harder to spot given tenant space impact.
Forward looking approach: A momentum csv output with year built and SF / DU along with residential vs. other tenant space break out by type and SF and would be helpful to look at systematically in order to spot outliers. For buildings without setbacks, a column with an automated Momentum check on SF that uses shape files would be amazing (because we can't yet do this for buildings with setbacks, correct Robin?)
Step 4: Utility data review
Backwards looking example: A few Related buildings have very high fossil fuel use relative to comps indicating potential cogen installation. Data quality errors (including wrong SF) can also contribute to this metric being off. I focus on this metric because it drives carbon footprint and fines - and is most correlated with decarbonization infrastructure changes.
Forward looking approach: A Momentum csv output with FF use intensity electricity use intensity and carbon fine per SF would help spot outliers.
Turning data integrity into Calls to Action (CTAs) #
Based on the above, any building in a portfolio should probably fall into one of these buckets
Bucket 0: Custom CTA: (1) Data is still funky and some human being needs to look at → could be benchmarking provider/Aurora/First Service OR (2) it is a snowflake building with a high fine that is probably best suited for a targeted feasibility study. For instance, fuel fired chiller buildings should probably get an engineering feasibility study to go from fossil fuel to electric chillers -- we could ultimately define scope and bid out such feasibility studies.... but not a top priority given small number of buildings
Bucket 1: No 2030 fine CTA: Probably nothing. Alternatively, for buildings that are relatively close to the fine threshold, they may want to consider Bucket 2 CTA steps as cheap insurance.
Bucket 2: Relatively lower 2030 fine (i.e. optimize of steam or hydronic distribution alone will get them under the 2030 fine threshold)
CTA:
(1) Share Momentum "Optimize" scope with decision maker (PDF output is the easiest thing to share but if someone wants to spend the time typing in each of the board members personal emails for every building a custom scope link could also be shared -- at which point, they will ask for a PDF anyway). Having a one pager piece of collateral and explainer video for steam vs hydronic video would be helpful. Why now: building optimization is the low hanging fruit but won't know for sure how low you can go until you put system in and operate -- important to establish a new baseline now to confirm you can operate under the 2030 limit with out additional major capital investment.
(2) If building owner wants to proceed, initiate generic project bidding process for optimize scope (which is basically a version of the Article 321 scope)
(3) Also bid out building SF measurement since these buildings are going to be the ones most on the bubble
CTA:
(1) Share Momentum BE 2030 compliance scopes with decision maker and alternate (much more painful) compliance scope that doesn't use the BE credit. Having a one pager piece of collateral and explainer vide for steam vs hydronic video would be helpful. Why now: waiting will cost more and be riskier.
(2) If building owner wants to proceed, initiate generic project bidding process for Optimize scope (if applicable)
(3) If building owner wants to proceed, initiate generic project bidding process for BE credit feasibility scope that would proceed deeper investment:
~5k feasibility study for DHW heat pumps and/or
~ $10-20k PTHP pilot involving installing units in a handful of apartments to assess fit and user experience
(4) if building owner wants to proceed, based on outcomes of feasibility study, initiate generic project bidding process for full BE credit scope
(5) perhaps also bid out SF measurement
(note that I didn't figure out how to work oil to gas conversion into the above logic but this is already much too long)
Summary of Non-Software Consulting Tasks #
💬 1 comment
Tasks That Can Be Automated with Software #
And for shits & giggles: Potential AI Contributions #