Stockholm

• Original plan listed 0 tCO₂ saved, lacked cost or coverage data, and could not be verified as published.

• The Gaia-enhanced version, using scientific baselines and realistic assumptions, achieved a 0.91% reduction (600,000 tCO₂) at €166.67 per tonne under the €100M simulated budget.

• Full feasibility simulations showed actual implementation costs of €431.9 million, indicating a 4.3x budget underestimation.

• No emissions savings or cost-per-tonne data in original plan

• Widespread use of high-cost retrofit models (€3000/t), only suitable for heritage buildings

• Sector coverage was unclear or incomplete; original plan included only 3 sectors

• Publish per-action emissions targets and cost breakdowns

• Clarify implementation timelines and demographic coverage

• Focus retrofit subsidies on optimised models, not high-cost heritage retrofits

• Integrate renewable energy more broadly across buildings and transport sectors

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.

• Lack of specific target reduction percentages for overall greenhouse gas emissions, which is crucial for measuring the plan's effectiveness.

• Estimated costs per unit and target savings in tonnes of CO2 for each action are not provided, making it difficult to assess financial feasibility and environmental impact.

• Coverage percentages are missing for some actions, which is necessary to understand the scope and impact of the initiatives.

• Include specific greenhouse gas reduction targets in line with IPCC recommendations to align with global efforts to limit warming to 1.5°C above pre-industrial levels.

• Provide detailed financial estimates and expected CO2 savings for each action to enhance transparency and enable better monitoring and evaluation.

• Define coverage percentages for all actions to clarify the extent of implementation and expected impact across the city.

- IPCC Special Report on the impacts of global warming of 1.5°C

- WHO Urban Health Initiative

Given the provided information, it's challenging to devise a comprehensive evaluation of Stockholm's Climate Action plan as the simulation result lacks key data. However, the essential element of a zero-cost estimate per tonne of CO2 reduced signifies a positive aspect of the strategy. This implies that the city might be identifying cost-neutral solutions (such as policy changes or behavior change initiatives) or that revenues generated from several reductions could be equal to or surpass their costs.

On the downside, major information gaps in Stockholm's climate simulation result are concerning. The absence of a specific CO2 savings value, budget allocation, and a target reduction percentage makes it impossible to properly evaluate the proposed plan's effectiveness, efficiency, or overall ambition. It's essential for successful climate planning to set clear, measurable goals to provide a robust framework for policy decisions and track progress towards achieving them.

Recommendations for Stockholm's climate team include firstly to define specific, measurable goals for CO2 savings and the targeted reduction percentage by 2040. Identifying the sectors that contribute the most to the city's emissions would help strategize priority areas effectively. Ensuring complete transparency regarding the allocated budget will help stakeholders understand the scope of the commitment and allow for more accurate assessments of cost per tonne. Simultaneously, looking for opportunities to incorporate cost-saving initiatives, such as energy efficiency measures or green infrastructure, could potentially offset costs and provide additional, multifaceted benefits to the city.

• Lack of detailed risk assessment and adaptation strategies for potential failures or delays in technology deployment.

• Insufficient integration of renewable energy sources beyond district heating, especially in the transport and buildings sectors.

• Coverage percentages are estimated without clear methodology or consideration of demographic and geographic variability within the city.

• Incorporate a comprehensive risk management framework that includes detailed scenario planning and adaptive management strategies, as recommended by the IPCC Special Report on Global Warming of 1.5°C.

• Expand the scope of renewable energy integration to include solar and wind energy in the transport and buildings sectors, as supported by the IEA's World Energy Outlook reports.

• Develop a more detailed methodology for calculating coverage percentages, ensuring it accounts for demographic and geographic differences, as suggested by the WHO guidelines on urban health metrics.

- IPCC Special Report on Global Warming of 1.5°C

- IEA World Energy Outlook

- WHO Urban Health Metrics

Based on the provided data for the Stockholm Climate Action GAIA Injected plan, there are some clear gaps and missing information that make a comprehensive analysis challenging. The strengths of the plan are not specified due to incomplete data. The fluctuations in CO2 savings, the budget allocated, and the target reduction percentage are undefined, which suggests either a high level of flexibility or a lack of detailed planning and oversight. Furthermore, the cost per tonne is projected to be zero, which while potentially ideal, does not seem plausible considering the financial requirements and resources usually needed for effective climate action.

As for weaknesses and inefficiencies, the major concern is the absence of outlined sectors. A detailed plan should delegate and categorize the actions across different sectors like energy, transport, waste, or buildings, for a more focused approach and to ensure that all areas contributing to carbon emissions are covered. The lack of specific numerical data also suggests an ill-defined strategy and failure in setting accountable targets.

In terms of recommendations, first and foremost, the plan needs to clarify its CO2 savings targets, the budget assigned, and the percentage reduction aim by 2040. These are fundamental aspects of a structured climate action plan. A proper cost estimate per tonne of CO2 should be arrived at through careful calculation encompassing all necessary expenditures. Identifying appropriate sectors for specific actions and detailing them in the plan could result in better resource allocation and increased effectiveness. The ultimate goal should be a balance between ambitious climate targets and realistic, actionable plans properly funded and sector-specific.

The simulation results reveal a promising range of climate actions with a total climate budget of €100,000,000 for Stockholm. Across sectors like Buildings, Energy, and Mobility, various strategies have been evaluated for their potential to reduce emissions, costs, and even improve the quality of life for citizens.

In the Buildings sector, strategies such as 'Deep retrofit for public buildings' and 'Subsidised insulation program' have demonstrated promising potential to reduce emissions and lower costs. However, the most effective measure appears to be the 'Deep retrofit of public buildings – Optimized', which boasts a superior cost per ton figure of only €256 and higher returns in terms of maintenance cost reduction, healthcare cost savings, and property value increase. It is crucial to consider the feasibility of retrofitting the number of buildings suggested by these actions, given the budget constraints and practical timelines.

Energy sector initiatives such as 'Utility-scale solar installation' and 'Offshore wind integration' offer substantial emissions reductions, but the standout measure is the 'District heating network deployment'. This measure, sourced from IEA Urban Energy Systems, not only reduces emissions significantly but also introduces ancillary benefits like maintenance cost reduction, healthcare cost savings, and a potential increase in property value.

In the Mobility sector, 'Electrify bus fleet' and 'Expand cycle lanes' are noteworthy actions, but 'Urban rail electrification and modal shift' proves to be most effective in terms of emissions reduction per cost, healthcare savings, and citizen satisfaction.

The simulation also reports an estimated average return on investment (ROI) impacts across all strategies, with a 3.04% reduction in maintenance costs, 2.62% savings in healthcare costs, 4.3% increase in property values, and a 5.92 point increase in citizen satisfaction scores. Additionally, these measures have the potential to mitigate urban temperature by an estimated 0.22°C, a significant step towards adapting to climate change. It is crucial to note that these actions are based on scientific evidence from respected sources such as the IPCC, IEA, EU, and UN. While the simulation provides an excellent starting point, the actual implementation would require a detailed feasibility study considering local conditions and constraints. This simulation was produced using Gaia Protocol, an open simulation engine based on scientific evidence and ROI optimization.

• Deep retrofit for public buildings – 2,845 tCO₂ @ €7,997,295 (€2811/t)

• Subsidised insulation program – 4,223 tCO₂ @ €7,998,362 (€1894/t)

• Deep retrofit of public buildings – Optimized – 31,250 tCO₂ @ €8,000,000 (€256/t)

• Deep retrofit of public buildings – Heritage-Grade Model (€3000/t) – 2,570 tCO₂ @ €7,997,840 (€3112/t)

• Deep retrofit of public buildings – Optimized – 30,303 tCO₂ @ €7,999,992 (€264/t)

• Deep retrofit of public buildings – Historical Conservation Model (€3000/t) – 2,834 tCO₂ @ €7,997,548 (€2822/t)

• Private building retrofit – Optimized – 15,873 tCO₂ @ €7,999,992 (€504/t)

• Private building retrofit – Standard – 10,825 tCO₂ @ €7,999,675 (€739/t)

• Private building retrofit – Ambitious – 9,269 tCO₂ @ €7,999,147 (€863/t)

• Utility-scale solar installation – 113,360 tCO₂ @ €83,999,760 (€741/t)

• Offshore wind integration – 59,030 tCO₂ @ €83,999,690 (€1423/t)

• District heating network deployment – 250,746 tCO₂ @ €83,999,910 (€335/t)

• District heating network deployment – 267,515 tCO₂ @ €83,999,710 (€314/t)

• Electrify bus fleet – 7,655 tCO₂ @ €7,999,475 (€1045/t)

• Expand cycle lanes – 15,873 tCO₂ @ €7,999,992 (€504/t)

• Urban rail electrification and modal shift – 28,368 tCO₂ @ €7,999,776 (€282/t)

The targeted sector for this operation is 'Buildings' with a total cost of €144000000. The cost per ton of CO2 reduction is estimated at €240. According to the International Energy Agency (IEA) Retrofit Guidelines, this commitment can be achieved with a delay of one year, suggesting a realistic timeframe for implementation without overreaching.

The potential return on investment (ROI) from this operation is notable. It includes a 6% reduction in maintenance costs, a 4% saving in healthcare costs, an 8% increase in property value, a 0.6°C mitigation of urban temperature, and a 7-point increase in citizen satisfaction scores. These figures are based on the simulation's "roi_summary", which weighs each factor's total and individual contributions to the overall ROI.

However, the estimated average ROI impacts suggest a slightly different picture. The urban temperature reduction, for instance, is estimated at 0.25°C, as opposed to the 0.6°C indicated in the initial results. The same is true for other ROI factors, with actual figures expected to be slightly lower than the initial estimates.

These figures underline the importance of managing expectations and planning appropriately for potential variances in outcomes. However, the overall direction of the results is positive and suggests that the city has a viable strategy for reducing CO2 emissions. The science behind this simulation aligns with the consensus of the Intergovernmental Panel on Climate Change (IPCC), the European Union, and the United Nations regarding the need for urban centers to reduce greenhouse gas emissions.

In conclusion, this simulation offers a robust and scientifically backed strategy for Stockholm to significantly reduce CO2 emissions, a crucial step in the fight against climate change. The potential benefits extend beyond emission reduction, with substantial ROI expected in areas such as maintenance costs, healthcare, property value, urban temperature, and citizen satisfaction.

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

In the Buildings sector, a range of actions are suggested, from deep retrofitting of public buildings to subsidising insulation programs. The most impactful action is the optimised deep retrofit of public buildings, which is estimated to reduce emissions by 32,520 tons and 26,755 tons in two separate instances, with a cost per ton of €246 and €299 respectively. It's important to note that these actions have both immediate and longer-term benefits, including reductions in maintenance costs, healthcare savings, increased property value, urban temperature mitigation and improved citizen satisfaction, as per the IEA Retrofit Guidelines.

The Energy sector offers significant opportunities for emission reductions. The most effective action in this sector is the deployment of district heating networks, with two instances reducing emissions by 276,315 tons and 224,000 tons respectively, as per the IEA Urban Energy Systems. Additionally, the integration of offshore wind and utility-scale solar installations are also proposed.

Mobility-related actions include electrifying the bus fleet, expanding cycle lanes, and electrifying the urban rail system and shifting modalities. According to IPCC Transport Summary and WHO Urban Transport Health 2020, these actions not only reduce emissions but also yield healthcare savings and increase citizen satisfaction.

While the simulation suggests a substantial potential for CO₂ reduction across these sectors, it is essential to ensure that the proposed actions are feasible in the real world. For instance, the deep retrofit of public buildings, especially heritage-grade and historical conservation models, may face practical constraints and require careful consideration and planning.

The return on investment (ROI) impacts are promising. Maintenance cost reduction averages at 3.04%, healthcare cost savings at 2.62%, property value increase at 4.3%, urban temperature mitigation at 0.22°C, and an average citizen satisfaction score increase of 5.92.

This comprehensive climate strategy, based on scientific data from reputable sources like the IPCC, IEA, EU, and UN, presents a feasible path for Stockholm to achieve its CO₂ reduction target, while also reaping multiple ancillary benefits.

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

• Deep retrofit for public buildings – 2,841 tCO₂ @ €7,997,415 (€2815/t)

• Subsidised insulation program – 4,851 tCO₂ @ €7,999,299 (€1649/t)

• Deep retrofit of public buildings – Optimized – 32,520 tCO₂ @ €7,999,920 (€246/t)

• Deep retrofit of public buildings – Heritage-Grade Model (€3000/t) – 2,758 tCO₂ @ €7,998,200 (€2900/t)

• Deep retrofit of public buildings – Optimized – 26,755 tCO₂ @ €7,999,745 (€299/t)

• Deep retrofit of public buildings – Historical Conservation Model (€3000/t) – 2,551 tCO₂ @ €7,997,385 (€3135/t)

• Private building retrofit – Optimized – 13,050 tCO₂ @ €7,999,650 (€613/t)

• Private building retrofit – Standard – 10,349 tCO₂ @ €7,999,777 (€773/t)

• Private building retrofit – Ambitious – 8,519 tCO₂ @ €7,999,341 (€939/t)

• Offshore wind integration – 70,116 tCO₂ @ €83,998,968 (€1198/t)

• District heating network deployment – 276,315 tCO₂ @ €83,999,760 (€304/t)

• District heating network deployment – 224,000 tCO₂ @ €84,000,000 (€375/t)

• Expand cycle lanes – 17,021 tCO₂ @ €7,999,870 (€470/t)

• Urban rail electrification and modal shift – 36,363 tCO₂ @ €7,999,860 (€220/t)

Based on IPCC AR6, IEA Roadmap, WHO Urban Health, EU Green Deal.

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.