Amsterdam

• Original Plan projected a 3.05 million tonne CO₂e reduction, at €1,114.75 per tonne, achieving just 1.74% toward climate neutrality targets.

• Gaia-Enriched Plan, using the same budget but fully verified data, yielded only 600,000 tonnes CO₂e saved (–80% impact), at a cost of €5,666.67 per tonne, revealing major overestimations in the original assumptions.

• Built Environment accounted for the largest share of emissions reduction (63.4%) in effective simulations, yet many proposed retrofits (e.g., “Berlin Model”) were flagged for unsustainable costs exceeding €3,000/tonne.

• Target ambition (1.74%) does not align with Net Zero pathways.

• Lack of unit cost data and missing integration with national strategies.

• Financial feasibility gaps and unrealistic implementation assumptions.

• Reassess the emissions reduction target in line with 1.5°C trajectories.

• Integrate adaptive planning and national/regional coordination.

• Improve cost transparency and financial sustainability measures.

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 Amsterdam’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 costs per unit for major actions, which complicates budget transparency and financial planning.

• The overall target reduction percentage (1.74%) appears significantly low for a climate neutrality goal, suggesting a possible miscalculation or misalignment with broader climate objectives.

• Lack of integration with broader regional or national climate strategies, which could lead to inefficiencies or overlaps in efforts.

• Include detailed financial estimates for each action to enhance transparency and improve stakeholder confidence in the plan's feasibility.

• Reassess the target reduction percentage to align with the Paris Agreement's goal of limiting global warming to well below 2 degrees Celsius, ideally 1.5 degrees.

• Develop stronger linkages with regional and national climate plans to ensure coherence and synergy in climate action efforts.

• Incorporate adaptive management strategies to allow for plan adjustments in response to new scientific findings and technological advancements.

• Lack of detailed risk assessment for project delays and cost overruns.

• Insufficient detail on funding sources and financial sustainability.

• Potential underestimation of societal and technological barriers in adoption rates.

• No clear adaptation strategies or resilience planning against climate impacts.

• Incorporate a comprehensive risk management framework to address potential delays and cost overruns.

• Detail financial mechanisms and ensure diversified funding to enhance financial sustainability.

• Conduct thorough stakeholder analysis to understand and mitigate barriers to technology adoption and behavioural change.

• Develop and integrate climate adaptation and resilience strategies into the main plan to cope with potential climate impacts.

- IPCC Special Report on the Impacts of Global Warming of 1.5 °C

- IPCC Sixth Assessment Report (AR6)

- World Health Organization (WHO)

- Health in the Green Economy

The simulation results for the city of Amsterdam, given a budget of €3,400,000,000 and a CO₂ reduction target of 3.0 Mt, present a varied set of action plans across multiple sectors. The actions cover Buildings, Energy, and Mobility sectors, and are based on validated sources such as the International Energy Agency (IEA), Intergovernmental Panel on Climate Change (IPCC), European Union (EU) strategies, and other global studies.

In the Buildings sector, the focus is on deep retrofits of public and private buildings, and an insulation program. The most impactful measure appears to be the optimized deep retrofit of public buildings, offering the greatest carbon reduction. However, its feasibility might be overestimated, considering the substantial number of buildings involved, which could exceed Amsterdam's public building stock.

In the Energy sector, a balanced combination of solar installations, offshore wind integration, and the deployment of district heating networks is proposed. These actions align with the EU's Renewable Energy Directive and the IPCC's Renewable Energy Sources and Climate Change Mitigation special report.

The Mobility sector highlights the electrification of the bus fleet, expansion of cycle lanes, and urban rail electrification with modal shift. These measures align with the EU's Green Deal and Sustainable and Smart Mobility Strategy, and the World Health Organization's guidelines for urban transport health.

The proposed actions overall provide a significant Return on Investment (ROI) beyond the mere cost-per-ton of carbon reduction. The average estimated impacts include a Maintenance Cost Reduction of 5.07%, Healthcare Cost Savings of 3.29%, Property Value Increase of 5.51%, and a Citizen Satisfaction Score increase of 6.0. Moreover, the Urban Temperature Mitigation potential is estimated at 0.29°C. These additional benefits reflect the value of comprehensive, multi-benefit climate action.

This simulation was produced using Gaia Protocol, an open simulation engine based on scientific evidence and ROI optimization. The strategy outlined here provides a robust starting point for low-carbon investment, but further refinement and local feasibility analysis are recommended to ensure successful implementation.

• Deep retrofit for public buildings – 150,000 tCO₂ @ €442,800,000 (€2952/t)
• Subsidised insulation program – 100,000 tCO₂ @ €162,500,000 (€1625/t)
• Deep retrofit of public buildings – Optimized – 7,201,970 tCO₂ @ €1,461,999,910 (€203/t)
• Deep retrofit of public buildings – Berlin Model (€3000/t) – 477,154 tCO₂ @ €1,461,999,856 (€3064/t)
• Deep retrofit of public buildings – Optimized – 5,355,311 tCO₂ @ €1,461,999,903 (€273/t)
• Deep retrofit of public buildings – Berlin Model (€3000/t) – 480,446 tCO₂ @ €1,461,997,178 (€3043/t)
• Private building retrofit – Optimized – 2,436,666 tCO₂ @ €1,461,999,600 (€600/t)
• Private building retrofit – Standard – 1,651,977 tCO₂ @ €1,461,999,645 (€885/t)
• Private building retrofit – Ambitious – 1,599,562 tCO₂ @ €1,461,999,668 (€914/t)

• Utility-scale solar installation – 200,000 tCO₂ @ €117,400,000 (€587/t)

• Offshore wind integration – 100,000 tCO₂ @ €123,700,000 (€1237/t)

• District heating network deployment – 3,283,489 tCO₂ @ €1,053,999,969 (€321/t)

• District heating network deployment – 3,072,886 tCO₂ @ €1,053,999,898 (€343/t)

• Electrify bus fleet – 150,000 tCO₂ @ €139,050,000 (€927/t)

• Expand cycle lanes – 50,000 tCO₂ @ €22,100,000 (€442/t)

• Urban rail electrification and modal shift – 4,376,237 tCO₂ @ €883,999,874 (€202/t)

In earlier simulations, the phrase "Berlin Model (€3000/t)" was automatically used to label a high-cost retrofit action. This label did not originate from any official Berlin policy, but rather emerged from repeated simulations during internal Gaia testing — where retrofit costs exceeding €3000 per tonne were frequently observed.

Gaia, as a learning system, identifies and names patterns that appear across plans, including problematic ones. However, these names do not indicate scientific endorsement. In this case, the label reflected a pattern of excessive retrofit cost assumptions, not a validated model.

Recognising this, the label has since been corrected to:

• "Heritage-Grade Model (€3000/t)"
• "Historical Conservation Model (€3000/t)"

These new terms acknowledge that such costs may occur in rare, heritage-protected buildings — but should not be applied broadly. Gaia will continue refining its logic as it learns from both well-founded and misapplied strategies across cities and simulations.