Data methodologies

The data explorers on this site cover global trade dynamics in natural resources and goods relevant to the circular economy, as well as national and subnational policies and roadmaps on the circular economy and the bioeconomy.

Policy data explorers

Policies included are divided into the following categories:

Circular economy

Circular economy roadmaps and strategies

This category includes circular economy roadmaps and strategies, actions and targets organized by: 

  1. National circular economy roadmaps and strategies:  Chatham House, with the support of the United Nations Industrial Development Organization (UNIDO), created the first comprehensive global stocktake of national circular economy roadmaps and strategies in 2024. The 2025 update analysed 99 national circular economy policy frameworks, including calls to action, roadmaps and operational strategies, and identified and thematically codified more than 4,300 individual policy actions by policy theme and by sector.
  2. Subnational circular economy strategies: This category includes regional and city-level subnational circular economy roadmaps and strategies.
  3. Quantitative targets: This category includes national-level quantitative targets conducive to the circular economy transition. 
Circular economy policy categories

This includes any national circular economy policies already in place, organized by: 

  1. Extended producer responsibility policy (national and state level): This category relates to policies that place the responsibility for the environmental impacts of products throughout the product life cycle on producers. It is often applied to the collection, processing and reuse of waste.
  2. Fiscal policy (national level): This category includes government tax and spending policies that incentivize circular practices.
  3. Product policy (national level): This category includes any policies that support circular practices relating to the design, manufacture, distribution or import of specific products and materials.
  4. Waste management and recycling policy (national and state/city level): This category covers policies that encourage circular practices relating to the management of waste, covering generation, segregation, transfer, sorting, treatment, recovery and disposal.
Circular Economy Action Plan policies

This category includes legislative acts and policies under the EU’s Circular Economy Action Plan (2020) and the forthcoming Circular Economy Act (2026) to support Europe’s transition to a resource-efficient, low-waste and climate-neutral economy. 

Other roadmaps and strategies

Chatham House has collated and mapped relevant national strategies that incorporate circularity – these include green economy strategies and national sustainable consumption and production strategies. 

Bioeconomy 

National bioeconomy roadmaps and strategies

Chatham House, with support from the World Bioeconomy Forum, has collated and published a map of national bioeconomy roadmaps and strategies to allow users to compare approaches, identify opportunities and support evidence-based development of sustainable, circular bioeconomies.

Trade data explorers

The trade data explorers have been developed by Chatham House to enable users to explore the fast-evolving dynamics of international trade in natural resources and circular economy goods, and the related interdependencies that emerge between importing and exporting countries and regions.

The natural resources trade data are from the Chatham House Resource Trade Database (CHRTD). The CHRTD is a repository of bilateral trade in natural resources between more than 200 countries and territories. The database includes the monetary values and weights of trade in over 1,350 different types of natural resources and resource products, including agricultural, fishery and forestry products, fossil fuels, metals and other minerals, and pearls and gemstones. It contains raw materials, intermediate products and by-products.

The circular economy trade data are from the Chatham House Circular Economy Trade Database (CHCETD). The CHCETD includes over 900 individual commodities organized principally into primary and secondary material categories. Primary materials featured include both raw and intermediate bioeconomy products – renewable resources from land and sea used for food, feed, construction and the creation of bioenergy. Here there is significant overlap with the agricultural and forestry products included in the CHRTD.

The methodology and underlying data source (UN Comtrade) behind both datasets are the same. Although there is some overlap in the commodities covered, the focus of the CHCETD is on primary and secondary resources that are of particular significance to the circular economy, whereas the focus of the CHRTD is on raw materials or relatively undifferentiated intermediate resources used by both linear and circular economic models.

Dealing with complexity

Bilateral statistics are critical to understanding global resource trade, but existing data are often difficult to access and use. The original data source for the CHRTD and CHCETD is International Merchandise Trade Statistics (IMTS). IMTS data are collected by national customs authorities and compiled into the United Nations Commodity Trade Statistics Database (UN Comtrade) by the United Nations Statistics Division. UN Comtrade uses three distinct trade classification systems: the Harmonized Commodity Description and Coding System (HS), the Standard International Trade Classification (SITC) and Broad Economic Categories (BEC). Of these, the CHRTD and CHCETD employ the HS taxonomy (1996 revision), which assigns HS codes to all forms of traded goods in a hierarchical structure (2-, 4- and 6-digit codes respectively represent commodity chapters, headings and subheadings).

Across the resource landscape there are alternative repositories of trade data available, but they do not offer the breadth and depth of analysis that UN Comtrade permits. For example:

  • Data from UN Trade and Development (UNCTAD) have the greatest temporal availability, with some aggregate categories dating back to 1948, but even more recent series lack commodity-level (6-digit HS code) detail.
  • The Food and Agriculture Organization of the United Nations (FAO) provides comprehensive agriculture, forestry, fisheries and aquaculture bilateral trade data, but not other resource domains. Much of the available data share the same origins as UN Comtrade and are not necessarily any more accurate.
  • The US Department of Agriculture’s Global Agricultural Trade System (GATS) is similarly domain constrained and focuses on US trading partners. (Similarly, the European Commission’s EUROIND database covers only trade with and between EU countries.)
  • The International Energy Agency (IEA) provides comprehensive energy balance and energy flow statistics, but trade statistics are limited to gas flows within, and to, Europe.
  • Data from the US Energy Information Administration (EIA) include national energy consumption, production, import and export statistics, but lack bilateral detail.
  • The world oil and gas databases developed by the Joint Organisations Data Initiative (JODI-Oil and JODI-Gas) were developed as transparency tools rather than trade databases. Unlike other databases, JODI does not adjust reported figures or substitute missing figures, so coverage is incomplete.
  • Commercial sources such as the Energy Institute's Statistical Review of World Energy provide comprehensive energy trade data, but no bilateral dimension.

UN Comtrade is therefore arguably the most comprehensive source of merchandise trade statistics available; volumetric and monetary value data are catalogued under more than 5,000 HS codes, and the monetary values of trades are available as far back as 1962. Monetary values are reported in current (nominal) US dollars. For an explanation of how these are derived by UN Comtrade from national reports denominated in national currencies, please see the UN ITS Knowledgebase. 

UN Comtrade does, however, present several challenges for users focusing on resource trade, which our data explorers address:

  • The HS system is not easy to use: the HS nomenclature has evolved historically as a pragmatic and comprehensive industrial taxonomy for the broad range of internationally traded goods.
  • The scale hinders simple queries: with over 3 billion trade records since 1962, finding the right data is not always easy and the size of data queries can be difficult to manage.
  • The IMTS data are of variable quality: missing data, trade mispricing, unreported and illegal trade, and general mistakes and inconsistencies all cast doubt over the reliability of certain reported trade flows.
  • The presence of between one and four data points for every trade flow complicates use: both exporters and importers are expected to report trade values and trade weights. If these records are incomplete or do not correspond with one another, there may be uncertainties about the reliability of data and/or about which reporter is the most authoritative.

Introducing clarity

The trade data on this site are reorganized around natural resources and the circular economy. As the IMTS and HS systems contain all types of traded goods – including manufactured goods – analysing natural resource trade flows in UN Comtrade typically requires amalgamating a variety of HS codes. The difficulty of this varies: products that have a long history of being traded extensively are captured in greater detail than products that are traded less frequently. For example, there is a single HS code associated with rare earth elements, but several hundred codes assigned to steel and steel products. The CHRTD overcomes this problem by selecting over 1,350 HS codes that are identifiable as raw materials or relatively undifferentiated intermediate products, and grouping them by resource type. For example, copper ores and concentrates, intermediate copper products such as mattes, bars or wires, and copper scrap are classified into a single ‘copper’ category, enabling global copper trade to be tracked at different stages of the value chain. The CHRTD employs a five-tier resource taxonomy permitting queries to be as atomized or aggregated as required.

The taxonomy employed by the CHCETD divides at the highest level into primary and secondary materials. Secondary materials included are those derived from both renewable and non-renewable resources. They include waste, scrap and residue as well as secondary raw materials and used goods. We include all the resources, and many more, covered by the EU Trade in Recyclable Raw Materials dataset.

It is widely acknowledged that the HS commodity classifications are imperfect for tracking secondary resource flows. At the most disaggregated HS 6-digit level, it is not always possible to distinguish primary and secondary flows, while some commodity codes simply do not differentiate between used, recycled or new products and others include waste, residue and scrap materials in the same commodity code as primary resource streams. In such instances, we have tended to favour inclusivity to represent all flows that are of potential significance to circular economy developments.

As such, the circular economy trade dataset should be regarded as experimental and we expect it to evolve over time as data availability improves. Nonetheless, we are not aware of any other public resources of comparable scope or ambition and our hope is that it serves as a valuable resource for all those interested in the circular economy.

Compiling the data

The Chatham House Resource and Circular Economy Trade databases employ a systematic approach to identify and manage data gaps and errors. The CHRTD and CHCETD are subject to the same data gaps and weaknesses as are apparent in other sources of international merchandise trade data. (For a full discussion of reporting asymmetries, see Markhonko, 2014.) However, the databases exploit the maximum information available within UN Comtrade to assess the reliability of individual trade records, and to present as complete and as reliable a picture as possible. The approach taken relies on two assumptions. First, for each trade flow the values (US$) and weights (tonnes) reported by the exporter and importer should approximate to one another. The reported monetary values are unlikely to be exactly the same since exports are typically reported on a free on board (fob) basis, whereas imports are typically reported on a cost, insurance and freight (cif) basis. Second, we expect the reported prices per tonne to relate to world market prices. Unlike some alternative approaches to reconciling importer and exporter reports, no assumptions are made about the general reliability of country reporting across multiple commodities or years; each individual report is assessed on its own merits.

Logical operations are used to produce a transparent decision on the relative reliability of each data point and to reconcile the importer and exporter reports into a single record. Each record incorporates the value and weight of the given commodity trade between the two countries in the given year. In each case we consider the degree of similarity between the importer and exporter reports. In cases where either trade partner reports the monetary value and the weight of the trade (some reports contain only the value), we perform a distributional analysis of the value-to-weight ratio for all trades of the given commodity in the given year, i.e. the reported price per tonne is assessed relative to the global distribution of unit prices for the same commodity in the same year. The vast majority of these unit price commodity-year distributions are lognormal in nature, so we take the natural logarithm of the unit prices to derive normal distributions (ln(US$/tonne)). Based on this transformation we identify outliers that are greater than three standard deviations from the mean value (μ ± 3σ). This results in approximately 1.5 per cent of the unit prices being flagged as outliers. This is illustrated in the figure below, which plots the 17 distributions for live sheep unit prices for 2000–16. Commodity-year combinations with fewer than 25 recorded trade flows are excluded, as too few observations remain for reconciliation to be meaningful.

Not all commodity-year distributions have a lognormal structure, however; some exhibit other structural properties that need to be accounted for. For example, where there are two distinct markets or where a commodity code amalgamates multiple distinct commodities, distributions may be bimodal. We therefore algorithmically identify whether secondary structures are present in the distribution and whether outlying data points require de-flagging as outliers. The number of bins is defined by the Freedman–Diaconis rule. Secondary structures are investigated by defining windows equal to one-twentieth of the total number of bins, in one-bin increments from the upper and lower bounds (μ ± 3σ). If any of these windows contain twice or more the expected number of data points (where the expectation is defined by the fitted normal distribution), the dataset within that window is defined as a secondary structure. However, if more than one of these structures is identified in either tail, we select the window that has the greatest difference between actual and expected number of data points as the secondary structure. Within a secondary structure, individual trade reports are only de-flagged as outliers if the natural logarithm of the reported value (ln$) is greater than the median natural logarithm value (med ln$) for the given commodity-year distribution, as at low values there is greater natural spread in the reported data.

The outcome of this process is illustrated in the figure below: 17 (2000–16) independent commodity-year distributions are shown for crude oil, with earlier years represented by darker shading. In the scatter plot, data points identified as outliers are shaded red-yellow, and data points that are within the bounds are shaded blue. Green-shaded data points are those that are initially identified as outliers but which are then de-flagged and treated as reliable on the basis of the secondary distributional structure identification process. This process is repeated for every commodity-year distribution. A sample of distributions with secondary structures identified by the algorithm is manually checked by researchers to ensure there is a valid rationale for their identification (e.g. the Norway-to-Canada trade highlighted below will be compared with other trade reports in the secondary structure to understand whether the data points represent plausible market dynamics). 

Three further tests refine this assessment. Records that appear to duplicate one another are set aside, and prices that look unusual but recur consistently on the same trade route are retained rather than treated as errors. Records lying more than five standard deviations from the mean are never de-flagged, however consistent the trading relationship or clear the secondary structure.

Following the outlier identification process, mirror reports of the same trade by both trade partners are reconciled into one report. If both partners report a non-outlying unit price, then a weighted average of the two reports is recorded; the weighting factor is calculated according to how far each reported unit price sits from the median unit price for that commodity in that year. Data points that are deemed unreliable and irreconcilable are labelled as such and quarantined.

Trades that cannot be reconciled are quarantined rather than discarded, and each carries a code recording why. The figures reported by both countries are retained alongside, so nothing is lost. A key to the codes is published with the data. 

Value and weight are assessed independently, so a trade may have a published value and a quarantined weight. The reverse does not occur: where no value can be established, no weight is published either.

Where the two reporting countries agree on the value of a trade but their reported weights differ substantially, one of the weights is unreliable. Which country’s figure to accept is determined from the consistency of its reporting for that trade route across the period, rather than from that year alone. Where neither can be preferred, no weight is published, though the value is retained.

Country groupings