Decision Support - Stage 3

01 Plan for multi-sector, integrated assessment of clean air and climate policies

Setting up integrated assessment model (IAM) or other decision support tools for effective air quality management planning requires careful consideration of staffing, expertise, and budget. First, assemble a multidisciplinary team (likely from across your staff and that of other departments) skilled in areas such as environmental science, economics, policy analysis, and data management. This team should include researchers, modelers, and communication specialists to ensure a well-rounded approach. 
Budgeting is crucial; allocate resources for software tools, training, data acquisition, and ongoing maintenance. Regular updates to the model are essential to accommodate new data and methodologies. 
Establish formal institutional arrangements for data reporting and assessment (See Emission Inventory Guidance Stage 1, Step 4) to ensure structured collaboration among stakeholders, including government agencies, academic institutions, and industry partners. Create clear protocols for data sharing, analysis, and reporting to facilitate transparency and credibility. Additionally, consider establishing a governance framework that includes regular stakeholder meetings to review model outputs, assess progress, and adapt strategies as necessary. This structured approach will enhance the effectiveness of IAMs in guiding air quality management planning. 

02 Select a tool

Integrated assessment tools like LEAP (Long-range Energy Alternatives Planning), GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies), ​ABaCAS (​Air Benefit and Cost and Attainment Assessment System), and others offer valuable insights for air quality management (AQM) planning, but each has its pros and cons. 
Each of these tools offer a range of comprehensiveness, allowing for a holistic view of air quality, energy use, and climate impacts, facilitating the exploration of various policy scenarios. They also enable evidence-based (data-driven) policymaking by modeling potential outcomes of different interventions, helping stakeholders prioritize actions and thus promote stakeholder involvement and public understanding. However, the models can be complicated and require significant expertise to interpret and apply results effectively. Their effectiveness depends heavily on the availability and quality of input data, which may be limited in some regions and implementing and maintaining these tools can be costly, requiring time and financial investment for training and data management. While the tools described above are publicly available at no cost, practitioners should be aware of several practical constraints that may apply to these or other modeling platforms. Some tools carry licensing fees or require proprietary software environments to operate, and certain platforms require that jurisdiction-specific model instances remain hosted on a remote server rather than residing on locally controlled infrastructure. Before committing to a modeling platform, agencies should assess not only the direct software costs but also the implications of any user agreements for data sovereignty, long-term access, and the ability to transfer or independently maintain the model as institutional capacity grows. Each agency will have to balance these factors in selecting a tool for successful implementation in AQM planning. 

03 Gather energy, inventory and economic data in format for your selected tool

Gather comprehensive data on local industries, transportation, and energy consumption, as these are key contributors to emissions. Refine data from Stage 2 on residential energy and waste. Assess existing emissions control measures by reviewing facility reports, compliance records, and technological installations. Document current and potential policies aimed at reducing emissions, including regulations and incentives.

To ensure sustainability of the process, establish a formal and ongoing data reporting framework to ensure consistent updates and stakeholder access to information. This framework should define data collection protocols, responsibilities, and timelines, facilitating collaboration among government agencies, industry, and researchers. Regularly review and refine the framework to adapt to changing conditions and emerging technologies, ensuring that air quality planning remains responsive and data-driven. ​A good example of this process for the LEAP framework is illustrated in Section 2.2 of Kuylenstierna et al. (2020) below for the country of Bangladesh.  Similarly, Annex D from the World Bank report (2025) below illustrates development and use of the GAINS model for Nepal.​​     ​ 

04 Develop energy, inventory and economic projections formatted for your selected tool

Transform national energy statistics into formats suitable for your selected tool by disaggregating national statistics into regional or sectoral data that align with your model’s structure. Focus on key variables such as energy production, consumption, emissions, and technology specifics. Ensure the data is time-series formatted, capturing trends over the chosen evaluation period. 
Next, adapt the data to fit the specific input requirements of the chosen model. This may involve unit conversions, defining functional relationships, and ensuring that all relevant variables are included. For example, if using LEAP, compile data by sectors such as transportation, residential, and industrial. Working with academic partners (see Stage 2, Step 10) who may have expertise and experience with the selected tools could significantly ease the burden of initial model setup.
After populating the model, calibration is essential to ensure its accuracy. This involves adjusting model parameters until the outputs align with recognized benchmarks or historical data. Compare model results with actual emissions data, energy consumption figures, and policy impacts to validate its accuracy. Sensitivity analyses can identify which parameters most influence outcomes, guiding further refinements. 
Regular updates and comparisons with new data will enhance the model's reliability over time, ensuring that it remains a robust tool for assessing air quality management strategies and policy effectiveness. Start by reaching out to energy officials/ sectoral colleagues to see if sectoral tools/projections exist that can provide greater sectoral detail. 

05 Conduct your baseline assessment

To conduct a baseline assessment with a GAINS model, input current emissions data, economic activities, and existing control measures. Ensure accurate representation of socio-economic factors, technologies, and policies. Analyze outputs to understand current air quality conditions and project future scenarios under "business-as-usual" trends, providing a foundation for evaluating potential interventions. To conduct a baseline assessment in a LEAP model, input historical energy consumption, emissions data, and current technologies across sectors. Define the existing policies and practices. Analyze the model outputs to establish a "business-as-usual" scenario, identifying trends and potential challenges, which will inform future planning and policy development.

06 Gather air quality monitoring, modeling, and source apportionment data and validate your tool

Validate your LEAP-IBC or GAINS (EMEP) model results with Stage 2 monitoring data and source apportionment results. Compare model outputs with observed pollutant concentrations. Analyze discrepancies to identify potential sources of error, such as inaccurate emission factors or data gaps. Use statistical techniques, like regression analysis and correlation coefficients, to quantify the relationship between model predictions and monitored data. Incorporating source apportionment results can further refine the model by allowing for targeted adjustments based on specific emission sources identified through monitoring. If you are using a GAINS model, ensure that the cost estimates for emission control technologies align with local economic conditions and market prices. Regularly review and update these estimates based on new research, industry reports, and case studies, ensuring the model reflects realistic implementation costs. 

07 Conduct your policy scenario

To develop a policy scenario in a LEAP or GAINS model that reflects key air quality (AQ) and climate policies, begin by identifying relevant local, national, and international regulations aimed at reducing emissions and improving air quality. Incorporate ambitious targets, such as commitments under the Paris Agreement or specific AQ standards. Identify potential measures, including the adoption of cleaner technologies, changes in fuel standards, and implementation of carbon pricing. After defining these policies, populate the model with necessary input data, such as cost estimates and technology adoption rates. Finally, simulate the scenario and analyze the outputs to evaluate potential impacts on emissions, air quality improvements, and compliance with climate goals.

08 Undertake policy prioritization through engagement and iteration with AQM decision-makers

Review model outputs, focusing on key findings related to emission reduction potentials, cost-effectiveness, health benefits, and environmental impacts. Establish clear prioritization criteria that consider factors such as effectiveness in reducing pollution, feasibility, health and economic impact, and alignment with existing regulations. Then engage stakeholders, including government agencies, public health officials, community organizations, and industry representatives, to gather diverse perspectives that ensure the process reflects community needs and values. Evaluate each policy option against agreed upon but established criteria, allowing for both qualitative and quantitative assessments of integrated assessment results. 
This evaluation will help identify high-priority policies that offer significant benefits and are feasible for implementation. Communicate the findings clearly to stakeholders, emphasizing the rationale behind selected policies and their anticipated impacts on air quality and public health. Finally, develop a draft implementation strategy with timelines, resource allocations, and assigned responsibilities, ensuring the prioritization process remains dynamic and responsive to new data and community feedback. This should help to effectively prioritize air quality policies that are grounded in evidence and aligned with community goals. 

09 Translate policy findings in layman terms for AQM decision-makers to share with policy officials and the public

To effectively translate findings from an air quality management (AQM) implementation plan into a publicly accessible justification, use clear and relatable language. Highlight key benefits of cleaner air, emphasizing improvements in public health, such as reduced respiratory illnesses (See Health Impact Assessment guidance), and the enhancement of quality of life through better recreational opportunities. Discuss ecological impacts, like healthier ecosystems and biodiversity, and note how cleaner air supports climate goals (See Environmental Impacts Assessment guidance). Present data on economic advantages, such as benefits for the aviation and tourism industry through improved visibility, and property values stemming from better air quality. Use visuals, infographics, and success stories to effectively convey messages, fostering community engagement and support for the policies and measures adopted.