Emissions Inventory - Stage 5

Key objectives for Stage 5
01 Update your Stage 5 plan
02 Strengthen partnerships across government, academia, and industry
03 Transition from Tier 2 to Tier 3 methods for key categories
04 Refine inventory granularity, sectoral and pollutant coverage, and temporal detail
05 Replicate Steps 3-10 from Stage 1 using Tier 2-3 methodologies
06 Expand emissions scenarios and policy evaluation systems
07 Transition to annual update, review, and reporting cycle
08 Establish in-house analytical and R&D capacity for continuous innovation

Stage 5: Establish a mature emissions inventory system with stable central funding, in-house technical capacity, secure data infrastructure, and annual updates

The Stage 5 emissions inventory should use Tier 3 methods for key categories and country-specific emission factors, including internationally derived factors that have been adapted and validated for national conditions and locally measured factors where available and appropriate, and more detailed pollutant information, such as VOC speciation, PM size fractions​, and chem​ical ​speciation​. At this stage, the inventory should serve as a core policy tool for reporting, refined air-quality modeling, policy tracking, accountability, scenario development, and continuous improvement.

Key objectives:

  • Develop and maintain a Tier ​1-2-​3 inventory using local data, measured emission factors, and advanced QA/QC​​ ​​while using Approach 2 for uncertainty estimation​​.​​​     ​
  • Expand technical detail to include SLCPs, VOC speciation, PM size fractions​ and chemical speciation​, and other policy-relevant pollutants.
  • Update the inventory annually and use it routinely for reporting, refined modeling, accountability, and policy formation; use advanced verification techniques such as inverse modeling. 

01 Update your Stage 5 plan

Stage 5 represents the point at which the emissions inventory is no longer being improved for reporting or technical analysis, but is operating as a permanent, policy-grade system that supports core government functions. Furthermore, the inventory works for both climate and air quality purposes, in a consistent manner covering air pollutants, greenhouse gases, and their intersection – short-lived climate pollutants (SLCPs) (see for instance Figure 1 in CCAC, 2023). The objective of this stage is to sustain the inventory over time while continuing to improve its accuracy, granularity, transparency, and usefulness for decision-making. This means maintaining the institutional arrangements established in earlier stages, strengthening the inventory’s role in planning and compliance systems, and ensuring that new methods, data sources, and scientific guidance can be incorporated into routine production workflows.

At Stage 5, the emissions inventory should operate as a fully mature, continuously improving, policy-grade system with permanent institutional ownership. Use the improvements identified in the previous cycle to update the plan and confirm governance arrangements, institutional mandates, management responsibilities, and the relationship between the inventory system and national planning frameworks. The plan should maintain multi-year commitments to staffing, budget, and infrastructure. Because a mature inventory supports reporting, planning, compliance, policy evaluation, and advanced modeling, the system should be treated as a core government function rather than a project activity and be centrally funded by the government. As an indicative benchmark, the mature system may require approximately 15 full-time-equivalent staff, including collaborators or contractors, together with an in-house data server, secure backup, and the computing infrastructure needed for routine modeling, analysis, and annual production. Define long-term priorities for inventory modernization, including further methodological improvements, automation, advanced spatial and temporal resolution, expanded pollutant coverage, and stronger integration with Air Quality Forecasting, Source ​​Attribution​​, Health Impact Assessment, and Decision Support.   

02 Strengthen partnerships across government, academia, and industry 

Formal partnerships across government agencies, research institutions, industry, and regional or international technical networks should support stage 5 inventories. Research institutions can help improve methods, evaluate uncertainty, design measurement studies, and support the development of Tier 3 methods for key categories. Industry collaboration is important for improving access to facility-level data, technology-specific activity data, control information, and measurement programs. These collaborations should be governed by clear data-sharing agreements, confidentiality provisions where needed, and QA/QC requirements (see IPCC, 2019) that ensure data can be used appropriately in the inventory. Establish partnerships with other government agencies or academia for intensive field campaigns, supersite studies, mobile monitoring, and source-specific measurement programs to improve Tier 3 methods. These activities should be aligned with national statistics, inventory categories, verification needs, and Source Attribution and Air Quality Monitoring Guidance. 

03 Transition from Tier 2 to Tier 3 methods for key categories where feasible

At Stage 5, apply Tier 3 methods for key categories where feasible (see IIED, 2023). These methods may include direct measurement, facility-level data, engineering models, intensive field campaigns, source-specific studies, or continuous monitoring systems. Tier 3 methods should be prioritized for sectors and pollutants that dominate total emissions, are highly uncertain, or are critical to national policy objectives. Use facility- and technology-specific methodologies, activity data, and emission factors wherever these data are available and quality-assured. Country-specific factors may be developed through local measurement or by adapting internationally derived factors to national technologies, fuels, operating practices, and control conditions, followed by appropriate validation. Document measurement methods, protocols, traceability, data coverage, and assumptions so that Tier 3 estimates can be reviewed and reproduced. Verify Tier 3 outputs against independent evidence where possible, including atmospheric observations (e.g., from aircraft and satellite remote sensing), in situ monitoring data, source attribution studies, or other external datasets (see text box below on “Inventory verification using atmospheric observations and inverse modeling” and IPCC 2019 Refinement Vol 1, Ch 6.10.2). This verification helps identify remaining biases and increases confidence in the inventory for policy and reporting applications. 

04 Refine inventory granularity, sectoral and pollutant coverage, and temporal detail 

Although no inventory is perfect, the necessary improvements for the future should be identified at the end of each inventory cycle. Mature inventories should provide the level of detail needed by policy users, air quality and forecast modelers, and assessment teams. Increase spatial resolution, facility type, technology classification, and temporal profiles where these improvements support air quality modeling, exposure analysis, policy tracking, or compliance applications (e.g., using tools such as US EPA SMOKE to prepare air quality modeling inputs like gridded inventories as described by Chapter 7 of the EMEP/EAA Guidebook). For pollutants, add detail such as PM and NMVOC speciation (e.g., using EPA SPECIATE tools), PM size fractionation, black carbon and organic carbon fractions, and toxicity-relevant PM indicators (e.g., chemical speciation) where available. These refinements can support advanced Air Quality Forecasting, Source Attribution, and Health Impact Assessment by providing more useful emissions inputs for atmospheric chemistry and exposure analysis. Improve temporal profiles for hourly, daily, monthly, and seasonal applications, and add emerging or previously minor sources where they become relevant. Align inventory outputs with the needs of modelers, policy analysts, regulatory users, and communication teams so that the inventory remains fit for purpose. 

05 Replicate Steps 3-10 from Stage 1 using Tier 2-3 methodologies

Using Tier 2 and Tier 3 methodologies (as appropriate), repeat the full inventory compilation process described in Stage 1 of the Emissions Inventory Guidance. At this maturity level, inventory recompilation should use advanced methods, improved activity data, refined emission factors, and systematic QA/QC procedures across all relevant pollutants and sectors. Quantify the impacts of methodological upgrades on time-series consistency (see IPCC Guidelines Chapter 5, 2019). Where methods or data sources change, use appropriate recalculation and splicing methods to preserve comparability of historical trends where possible. Update uncertainty assessments using Monte Carlo methods, and revise key category analysis accordingly, and the resulting improvements for the next cycle. Continue reporting to UNFCCC, EU, LRTAP, and other relevant regional agreements, while also producing advanced spatialized outputs, including GIS maps and high-resolution gridded inventories. Maintain complete documentation and archives so that the inventory is suitable for technical review and future updates. 

06 Expand emissions scenarios and policy evaluation systems

At Stage 5, the inventory should support advanced scenario analysis and policy evaluation before and after policy adoption. Quantify expected impacts of future policies relative to business as usual (BAU) scenarios, including changes in technologies, fuels, activity levels, standards, permitting requirements, and compliance programs. Integrate EI outputs into regulatory systems that support emissions standards, permitting, compliance programs, and the Air Quality Management Plan (AQMP) discussed in the Legal Framework and Policy Design Guidance. Inventory results should help evaluate whether regulations are achieving expected reductions and where additional controls may be needed. Evaluate policy co-benefits for climate, air quality, health, ecosystem services, natural capital, and other environmental impacts in coordination with Health Impact Assessment, Decision Support, Environmental Impact Assessment, and Legal Framework & Policy Design teams. Updated inventory evidence should support adaptive policy design and continuous improvement of air quality management strategies. Tools for scenario development and analysis are available (e.g., TAPS scenario tool and integrated assessment models such as ​air quality models coupled to health/economic assessment tools, ​GCAM and GAINS). 

07 Transition to annual update, review, and reporting cycle 

A fully mature inventory system should produce annual releases on a predictable schedule (e.g., at the same time each year or in line with reporting schedules, such as the UNFCCC National Inventory Reporting or LRTAP cycles). Annual updates increase the inventory's usefulness for policy tracking, public reporting, domestic and international information and communication, and rapid assessment of changing emissions patterns. Synchronize domestic and international reporting calendars where possible, and update key datasets and indicators on a routine basis. Regular publication supports transparency and public confidence, particularly when coordinated with Public Engagement & Communication activities. Apply formal protocols for recalculation, splicing, documentation, and archiving to ensure technical review and time-series consistency. Each release should clearly describe methodological changes, data updates, recalculations, limitations, and planned improvements for the next cycle. 

08 Establish in-house analytical and R&D capacity for continuous innovation

At Stage 5, agencies should maintain in-house analytical and research capacity to support continuous innovation in inventory. A defined portion of the centrally funded inventory program should therefore support in-house research, method evaluation, pilot studies, and the incorporation of validated scientific advances into routine inventory production. Develop internal expertise in methods, modeling, automation, QA/QC, data analytics, and spatial analysis so that the inventory team can evaluate and incorporate new information efficiently. Pilot new data sources such as remote sensing (e.g., see NASA ARSET air quality trainings), continuous emissions monitoring systems (CEMS), smart meters, facility databases, mobility data, and other high-resolution or big-data products where they are relevant and quality assured. Test machine-assisted QA/QC and workflow automation to improve efficiency, reduce errors, and accelerate routine updates. Automate recurring data ingestion, validation, calculation, reporting, and archiving workflows where feasible. ​Machine-assisted tasks and automation will be helpful for first-level tasks, ​but human​ verification is critical to review and provide final data and approvals. ​Maintain the capacity to rapidly incorporate new reporting guidelines, scientific methods, and emerging data sources into production workflows. This capability will help the inventory system reflect advances in science and technology while maintaining transparency and reliability. See the text box below for a highlight on using atmospheric observations and inverse modeling to verify emissions inventories.

Finally, a jurisdiction operating at Stage 5 should also contribute its expertise back to the broader air quality management community. Where appropriate, agencies should share methods, lessons learned, technical documentation, case studies, and non-confidential datasets through peer-learning networks, regional technical exchanges, publications, training events, or platforms such as the IPCC’s Emission Factor Database, the Community Earth-atmosphere Data System (CEDS), or the AQMx Resource Exchange Library. Sharing experience with other jurisdictions can help improve inventory methods globally, accelerate the adoption of good practices, and support capacity-building in countries or cities at earlier stages. A mature EI system should therefore not only benefit domestic planning and reporting, but also contribute to the global knowledge base for air quality management. 

Inventory verification using atmospheric observations and inverse modeling

This box describes using atmospheric observations to verify the Emissions Inventory as an additional QA/QC check.  Additional information can be found in the IPCC 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Section 6.10.2.

While traditional inventory emissions are built from the ‘bottom-up’, adding up information from known sources by tracking activity data and emission factors, atmospheric verification moves from the ‘top-down’, by starting with measuring what is in the atmosphere and deducing what emissions were needed to produce a given concentration of an atmospheric pollutant.  Comparing an inventory to emissions deduced from a ‘top-down’ approach is not always ‘apples-to-apples’, and ‘top-down’ derived emissions are not a replacement for an inventory.  However, comparing the two methods provides an independent line of evidence that an inventory is broadly reasonable if emissions are missing or misallocated and if emission trends over time align with observed trends.

At a basic level, ‘top-down’ methods, often referred to as inverse modeling, are straightforward.  Monitoring instruments, from the ground, from aircraft, or, most often, from satellites, measure the amount (concentration) of a pollutant or gas in the air at a specific place and time.  A chemical transport model (CTM) ​(also broadly ​referred​ to as an Air Quality Model, which incorporates all physical and chemical processes, in contrast to an air dispersion model that only incorporates physical processes such as dispersion) ​is used to understand where the air at that site came from, and what was already in the air when it arrived.  By statistically combining information from the CTM with data from the monitoring instruments, the inverse model can determine how much of a pollutant had to be emitted in the model to reproduce the observed concentrations.  

For inverse modeling to be useful for emissions inventories, the inventory needs to be incorporated into the chemical transport model as a spatially gridded dataset, preferably with relevant time-series granularity (e.g., variability by month, season, day, or hour, as appropriate for each pollutant). When ‘top-down’ and ‘bottom-up’ emissions agree within their uncertainty ranges, there can be additional confidence in the inventory.  At the same time, results from inverse modeling can indicate when observations and inventories have spatial, temporal, or trend mismatches.  When this happens, these differences should not automatically indicate an inventory error, but should be investigated.  Possible explanations include missing emission sources, incorrect activity data or emission factors, poor spatial allocation, incorrect temporal assumptions, measurement limitations, and CTM errors (e.g., poorly modeled atmospheric transport or unaccounted-for transboundary pollution transport).  

Not all pollutants are suitable for ‘top-down’ approaches.  Inverse modeling is more feasible for pollutants that are directly emitted, routinely measured, and have sufficiently long lifetimes (residence times in the atmosphere) such that their emissions and atmospheric concentrations are clearly related.  Prime candidates include methane, carbon monoxide, sulfur dioxide, nitrogen oxides, and some primary particulate matter.  Highly reactive, short-lived pollutants, or those formed within the atmosphere (secondary pollutants), such as ground-level ozone, are more difficult.  Importantly, a sufficient number of high-quality observations is critical to inverse modeling, as is a well-tested CTM with clearly reported uncertainty.  Additional information on inverse modeling, especially as applied to methane, can be found in Jacob et al., 2016 and 2022. 

This guidance document was prepared by Cynthia A. Randles (Randles Ozkul Consulting) under the overall oversight of the Climate and Clean Air Coalition Secretariat. The CCAC wishes to thank Charbel Afif (Professor, Saint Joseph University) and AQMx TAG Emissions Inventory expert, for his valuable feedback.