New York

New York City is the United States’ most populous city, with approximately 8.48 million residents, a GDP of $1.29 trillion, and major sectors in finance, media, technology, healthcare, and tourism. The city has reduced per‑person CO₂ emissions to around 7.1 t due to dense urban form and public transit, yet it remains exposed to climate threats—extreme rainfall, sea‑level rise, storm surge, heatwaves, and aging infrastructure. The city faces challenges such as flood and stormwater system overloads, coastal storm surge risk, extreme heat events, air pollution hotspots, and inequitable vulnerability across boroughs; strengths include a world‑class transit network, progressive climate legislation (e.g., Local Law 97), diversified economy, and strong climate institutions. Its climate adaptation efforts include the New York City Panel on Climate Change (NPCC), PlaNYC and VIA vulnerability assessments, extensive flood defences, green infrastructure retrofits, building emissions retrofit laws, congestion pricing, and MTA resilience investments.

Population: 8480000

Emissions per Capita: 7.1 tCO₂e

Total Emissions: 60208000

Vulnerability Score: 0.62

• The original plan listed a 100% reduction target, but provided 0 tCO₂ savings and no cost estimates, making the plan non-evaluable in its published form.

• Gaia simulated feasibility using a default €100 million budget (since no budget was disclosed). With full data injected, the CO₂ reduction was just 2.82% (49.25 Mt) at a cost of €2.03/tonne, suggesting massive underreporting of financial needs.

• A strategic simulation revealed that a realistic cost of €14.5 billion would be needed to achieve the full 49.25 Mt reduction target. Budget-constrained implementation achieved only 1.13 Mt (~2.3%).

• Buildings: 49.1%

• Energy: 28.4%

• Mobility: 12.3%

• Circular Economy: 10.1%

• Original plan lacks:

• Sector-specific CO₂ reduction targets
• Cost-per-unit estimates
• Realistic financial models or feasibility thresholds

• Multiple retrofit scenarios included duplicated actions and unrealistic heritage-grade costs (€3000+/t) flagged by Gaia for review

• Publish full sector-by-sector emissions targets and estimated cost curves

• Calibrate retrofits using NYC-specific building data

• Integrate sensitivity analysis and feasibility studies for core actions

• Expand citizen consultation and implement transparent tracking metrics

Gaia analyzes real-world climate plans by simulating:

1. 📉 CO₂e reduction potential
2. 💸 Cost per tonne and ROI
3. 🏥 Healthcare and social co-benefits
4. 🏗️ Infrastructure savings and property uplift
5. 🌡️ Urban temperature mitigation
6. 🧠 Citizen trust and satisfaction outcomes

It does so using:

• Verified scientific baselines (IPCC, AR6, WHO, IEA, Eurostat, etc.)

• Modular rule logic and impact trees

• IoT, satellite, and citizen feedback data streams (when operational)

• AI-powered simulation layers tied to ethics-bound regenerative logic

Gaia is designed to run in a high-security, air-gapped HPC environment with DMZ-controlled gateways. This architecture ensures:

• 🔒 Protection from manipulation
• 🔁 Verifiable, auditable simulations
• 🧾 Immutable public-facing ledgers for regeneration tracking

It is not a marketplace, a crypto token, or a carbon credit trading system. It is a truth layer for climate policy, capable of identifying real impact, revealing false optimism, and helping cities reach Net Zero with clarity, speed, and integrity.

This document presents an independent simulation and analysis of the city’s official Net Zero plan using the Gaia Protocol Evaluation Engine. Our goal was not to critique the city’s ambition, but to test its feasibility, completeness, and efficiency, and to show how advanced modelling and verified climate data can enhance results while saving cost, time, and emissions.

To do this, we followed the process below:

✅ Step-by-Step Methodology

1. Data Extraction We extracted raw action data from the city’s published Net Zero plan and structured it into a standardized JSON dataset.
2. Initial Gaia AI Review We ran the original plan through Gaia’s Evaluation Engine to identify gaps, inconsistencies, or missing elements based on global best practices.
3. Data Injection (Gaia Enhancement) We filled in the missing data using authoritative, peer-reviewed sources including: IPCC AR6 IEA & UN climate datasets WHO health impact baselines EU-level carbon pricing and building standards
4. Enhanced Plan Evaluation We re-ran the now-completed dataset through Gaia to produce a Gaia Enhanced Simulation , a cleaner, more complete version of the same plan, using the same budget and sector goals.
5. Side-by-Side Comparison We compared the original and enhanced plans based on: Projected CO₂e offsets Cost per tonne Sector-level impact ROI across infrastructure, health, housing, and public satisfaction
6. Modular Simulation Scenarios We then tested the plan’s internal logic using Gaia’s modeling engine in three configurations: Budget + Sectors only (not required) CO₂ Target + Sectors only (not required) Budget + CO₂ Target + Sectors

These simulations were run independently of political assumptions or formatting inconsistencies. The goal was to simulate what would happen if each plan were implemented exactly as described, and how performance changes when data is complete.

• Absence of estimated cost per unit for all planned actions, which is essential for budget planning and financial feasibility assessment.

• Lack of specified target carbon savings for each action, making it difficult to measure the plan's effectiveness in achieving carbon neutrality.

• Despite listing a 100% reduction target, no quantified total CO₂ offset is provided, which prevents reliable modeling and trajectory analysis.

• Incorporate detailed financial estimates per action to enable cost-effectiveness evaluation and funding prioritisation.

• Provide explicit carbon savings estimates for each action and sector to enable scenario modelling and impact forecasting.

• Establish a clear, quantified CO₂ offset target (e.g., in megatonnes) aligned with the 100% reduction goal for transparency and validation purposes.

- https://www.ipcc.ch/sr15/

- https://unfccc.int/process-and-meetings/the-paris-agreement

-https://www.nyc.gov/assets/sustainability/downloads/pdf/publications/Carbon-Neutral-NYC.pdf

However, the policy analysis reveals a few deficiencies that could diminish its overall effectiveness. The lack of crucial figures such as CO2 savings, budget, and the Target Reduction by 2050 provides little clarity on the practical aspects of the plan's implementation and expected outcomes. Without these essential parameters, a realistic and measurable framework for success may be difficult to construct. Furthermore, the omission of sectors affected and involved in the plan is also of concern, as it is necessary to address varied inputs and impacts across sectors for comprehensive climate action.

Recommendations for enhancing the proposed plan comprise the inclusion of articulated CO2 reduction targets, budgeting, and an outline of how different sectors would contribute and be influenced. Establishing an understanding of budgetary requirements would allow the city to strategically allocate funds to areas where they would bear the maximal benefit. Developing sector-specific strategies could concentrate efforts where they are most needed, ensuring both high efficiency and equitable impacts. Lastly, I would recommend employing market-based instruments, like carbon pricing, renewable energy credits, and cap-and-trade schemes, to lower costs and incentivize participation. Such measures would ensure that the pathway to a carbon-neutral New York City is both feasible and economically viable.

• The target reduction percentage of 2.82% is significantly lower than expected for a comprehensive plan aimed at achieving carbon neutrality by 2050.

• The estimated costs and savings in CO2 emissions for some actions lack detailed breakdowns or fail to account for dynamic market conditions and technological advancements.

• Coverage percentages for some sectors seem overly optimistic or are not adequately justified with current infrastructure capabilities.

• Revise the target reduction percentage to reflect the comprehensive nature of the plan and align with global standards for carbon neutrality, as outlined in the IPCC's Special Report on Global Warming of 1.5°C.

• Include more detailed financial models and sensitivity analyses to better predict costs and savings over time, taking into account potential technological advancements and economic factors.

• Conduct feasibility studies to ensure the infrastructure and regulatory support for the planned coverage percentages, particularly in the transport and fuels sectors.

- IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty

- World Health Organization, 2018: Health, environment and climate change

On the other hand, the plan's inadequacies primarily lie in its lack of detailed numerical goals. Without a specified budget, CO2 savings goal, or targeted reduction percentage, it becomes difficult to measure progress or success. Moreover, the notion of a zero-cost for each tonne of CO2 reduction might imply overoptimism or under-accounting of real costs involved. It's crucial to realistically assess and allocate resources to combat climate change efficiently.

To enhance the plan, we recommend that the NYC City Council establish precise and quantified objectives for each sector. Transparent numeric goals create accountability and allow the city to track progress effectively. Additionally, pricing mechanisms for carbon offset should be explored in depth. While cost-efficiency is ideal, underestimation of costs involved might lead to budget overruns in the future. Competition in green technologies and carbon sequestration can be encouraged by providing incentives, promoting innovation while driving costs down. Lastly, public consultation and increasing civic engagement could garner broader support and help diversify the city's climate initiatives.

The strategy primarily focuses on the building and energy sectors, with additional interventions in mobility and the circular economy. In the building sector, a series of retrofits and insulation programs are proposed, with the most impactful being the optimized deep retrofit of public buildings.

This action alone is projected to reduce carbon emissions by an impressive 296,116 tons. However, it's important to note that this figure might be an overreach given the city's current capacity for retrofitting public buildings. In the energy sector, the integration of offshore wind and utility-scale solar installations, coupled with the deployment of district heating networks, are expected to bring substantial emission reductions. The circular economy and mobility sectors also contribute to the overall emission reduction, with actions including city-wide waste reuse incentives, low-carbon material substitution in construction, bus fleet electrification, and the expansion of cycle lanes.

The strategy also estimates a range of return on investment (ROI) impacts. It anticipates maintenance cost reductions averaging 4.6%, healthcare cost savings around 2.53%, and an increase in property values by about 5.09%. The interventions are also predicted to mitigate urban temperatures by approximately 0.29°C, contributing to the city's resilience to heatwaves and urban heat island effects. The citizen satisfaction score is expected to increase by 5.91 points, reflecting the overall positive impacts of the strategy on the quality of life.

This simulation was produced using Gaia Protocol, an open simulation engine based on scientific evidence and ROI optimization. The results provide a valuable guide for strategic climate action, but should be assessed in conjunction with the city's existing capacities and priorities. Further research and stakeholder consultation are recommended to tailor this strategy to the specific context of New York City.

• Deep retrofit for public buildings – 19,291 tCO₂ @ €60,998,142 (€3162/t)

• Subsidised insulation program – 33,571 tCO₂ @ €60,998,507 (€1817/t)

• Deep retrofit of public buildings – Optimized – 296,116 tCO₂ @ €60,999,896 (€206/t)

• Deep retrofit of public buildings – Heritage-Grade Model (€3000/t) – 20,594 tCO₂ @ €60,999,428 (€2962/t)

• Deep retrofit of public buildings – Optimized – 231,060 tCO₂ @ €60,999,840 (€264/t)

• Deep retrofit of public buildings – Historical Conservation Model (€3000/t) – 21,685 tCO₂ @ €60,999,905 (€2813/t)

• Private building retrofit – Optimized – 104,452 tCO₂ @ €60,999,968 (€584/t)

• Private building retrofit – Standard – 67,777 tCO₂ @ €60,999,300 (€900/t)

• Private building retrofit – Ambitious – 69,873 tCO₂ @ €60,999,129 (€873/t)

• Utility-scale solar installation – 50,083 tCO₂ @ €29,999,717 (€599/t)

• Offshore wind integration – 20,422 tCO₂ @ €29,999,918 (€1469/t)

• District heating network deployment – 103,806 tCO₂ @ €29,999,934 (€289/t)

• District heating network deployment – 116,279 tCO₂ @ €29,999,982 (€258/t)

• Electrify bus fleet – 4,038 tCO₂ @ €4,999,044 (€1238/t)

• Expand cycle lanes – 9,596 tCO₂ @ €4,999,516 (€521/t)

• Urban rail electrification and modal shift – 18,939 tCO₂ @ €4,999,896 (€264/t)

• City-wide waste reuse incentives – 5,738 tCO₂ @ €3,999,386 (€697/t)

• Low-carbon material substitution in construction – 22,222 tCO₂ @ €3,999,960 (€180/t)

In the Circular Economy sector, utilizing low-carbon material substitution in construction could result in a reduction of 5 Mt of CO₂, costing €1.1 billion. This action also has the potential for a 1% reduction in maintenance costs, healthcare savings, and property value increase, with a citizen satisfaction score increase of 2.

The Buildings sector could see a substantial reduction of 20 Mt of CO₂ through the deep retrofitting of public buildings, at a cost of €4.8 billion. The ROI includes reductions in maintenance costs by 6%, healthcare savings of 4%, an 8% increase in property value, a 0.6°C urban temperature mitigation, and a citizen satisfaction score increase of 7.

In the Mobility sector, urban rail electrification and modal shift, along with the expansion of cycle lanes, could result in a CO₂ reduction of 6.05 Mt, with a total expenditure of €1.525 billion. The ROI from these actions includes maintenance cost reductions, healthcare savings, property value increase, and a positive impact on the citizen satisfaction score.

The Energy sector could achieve a reduction of 14 Mt of CO₂ through the deployment of district heating networks, costing €4.55 billion. The ROI includes 4% reduction in maintenance costs, 5% in healthcare cost savings, 4% property value increase, a 0.2°C urban temperature mitigation, and a citizen satisfaction score increase of 6.

Despite these promising figures, it's crucial to consider the feasibility and scalability of these actions. For instance, the deep retrofitting of public buildings and the deployment of district heating networks are repeated twice in the simulation, which may overestimate the practical potential of these actions.

The average ROI impacts are estimated as 3.94% for Maintenance Cost Reduction, 2.47% for Healthcare Cost Savings, 4.73% for Property Value Increase, and a 0.24°C reduction in Urban Temperature. The Citizen Satisfaction Score is estimated to increase by 5.38.

This comprehensive strategy, grounded in scientific evidence from globally recognized sources, presents a robust approach to achieving New York City's ambitious climate goals. However, careful consideration of the feasibility and practical implications of these actions is necessary for the successful implementation of this strategy.

This simulation was produced using the Gaia Protocol, an open simulation engine based on scientific evidence and ROI optimization.

• Deep retrofit of public buildings – Optimized – 10,000,000 tCO₂ @ €2,400,000,000 (€240/t)

• Private building retrofit – Optimized – 4,200,000 tCO₂ @ €2,520,000,000 (€600/t)

• Urban rail electrification and modal shift – 6,000,000 tCO₂ @ €1,500,000,000 (€250/t)

• Expand cycle lanes – 50,000 tCO₂ @ €25,000,000 (€500/t)

In the Buildings sector, actions such as deep retrofits of public buildings, subsidized insulation programs, and private building retrofits can lead to significant CO₂ reductions. However, it is important to note that some actions may be overreaching, such as the deep retrofit of public buildings – Optimized, which is duplicated in the simulation and may be unrealistic due to the physical number of public buildings available for retrofit.

Similar caution should be exercised in the Energy sector, where the deployment of district heating networks is also duplicated in the simulation. Nevertheless, utility-scale solar installation and offshore wind integration are feasible and impactful actions that align with the IPCC AR6 and EU Offshore Strategy respectively.

In the Mobility sector, actions like bus fleet electrification, cycle lane expansion, and urban rail electrification can contribute to CO₂ reductions and citizen satisfaction, as outlined by the WHO Urban Transport Health report and the EU Cycling Strategy.

Lastly, in the Circular Economy sector, low-carbon material substitution in construction and city-wide waste reuse incentives can help reduce emissions and save costs, as recommended by the UNEP Resource Efficiency Pathways and the EU Circular Economy Action Plan.

The simulation also estimates average ROI impacts, including a 4.6% reduction in maintenance costs, 2.53% savings in healthcare costs, a 5.09% increase in property values, a 0.29°C mitigation in urban temperatures, and a 5.91 point increase in citizen satisfaction scores.

It is crucial to remember that these actions and their impacts are interdependent and must be strategically planned and executed to optimize their benefits. This simulation was produced using Gaia Protocol, an open simulation engine based on scientific evidence and ROI optimization.

• Deep retrofit for public buildings – 21,041 tCO₂ @ €60,997,859 (€2899/t)

• Subsidised insulation program – 33,553 tCO₂ @ €60,999,354 (€1818/t)

• Deep retrofit of public buildings – Optimized – 237,354 tCO₂ @ €60,999,978 (€257/t)

• Deep retrofit of public buildings – Heritage-Grade Model (€3000/t) – 20,313 tCO₂ @ €60,999,939 (€3003/t)

• Deep retrofit of public buildings – Optimized – 220,216 tCO₂ @ €60,999,832 (€277/t)

• Deep retrofit of public buildings – Historical Conservation Model (€3000/t) – 21,261 tCO₂ @ €60,997,809 (€2869/t)

• Private building retrofit – Optimized – 107,964 tCO₂ @ €60,999,660 (€565/t)

• Private building retrofit – Standard – 71,012 tCO₂ @ €60,999,308 (€859/t)

• Private building retrofit – Ambitious – 63,541 tCO₂ @ €60,999,360 (€960/t)

• Utility-scale solar installation – 56,074 tCO₂ @ €29,999,590 (€535/t)

• Offshore wind integration – 21,739 tCO₂ @ €29,999,820 (€1380/t)

• District heating network deployment – 81,521 tCO₂ @ €29,999,728 (€368/t)

• District heating network deployment – 119,047 tCO₂ @ €29,999,844 (€252/t)

• Electrify bus fleet – 5,422 tCO₂ @ €4,999,084 (€922/t)

• Expand cycle lanes – 11,990 tCO₂ @ €4,999,830 (€417/t)

• Urban rail electrification and modal shift – 18,382 tCO₂ @ €4,999,904 (€272/t)

• City-wide waste reuse incentives – 5,856 tCO₂ @ €3,999,648 (€683/t)

• Low-carbon material substitution in construction – 17,316 tCO₂ @ €3,999,996 (€231/t)

This report includes one or more retrofit actions labeled as:

• “Heritage-Grade Model (€3000/t)”

• “Historical Conservation Model(€3000/t)”

These action types represent atypically high-cost retrofit scenarios, generally only appropriate for protected buildings or architecturally constrained heritage zones.

Gaia includes these profiles not as recommendations, but as part of its evolving knowledge base. These models emerged through repeated simulations across multiple cities, where retrofit assumptions far above international benchmarks were detected and flagged.

Gaia learns from pattern frequency — but does not endorse patterns that conflict with empirical data. By naming and isolating these edge-case models, Gaia enables transparent evaluation and prevents inflated assumptions from being applied indiscriminately.

As the platform matures, Gaia will continue refining these profiles using verified costs, city-specific constraints, and architectural typologies.