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The CHRTD is a repository of bilateral trade in natural resources between more than countries and territories. The database includes the monetary values and masses of trade in over 1, 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. Bilateral statistics are critical to understanding global resource trade, but existing data are often difficult to access and use. UN Comtrade utilizes three distinct trade classification systems: Of these, the CHRTD employs the HS taxonomy 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. UNCTAD data have the greatest temporal availability, with some aggregate categories dating back to , but even more recent series lack commodity-level 6-digit HS code detail.

UN FAO provides comprehensive agriculture, forestry, fisheries and aquaculture bilateral trade data, but not other resource domains. Much of the available data shares the same origins as UN Comtrade and is not necessarily any more accurate.

IEA provides comprehensive energy balance and energy flow statistics, but trade statistics are limited to gas flows within, and to, Europe. Unlike other databases, JODI does not adjust reported figures or substitute missing figures, so coverage is incomplete. 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, HS codes, and the monetary values of trades are available as far back as However, it does present several challenges for users focusing on resource trade, which the CHRTD and the resourcetrade.

The HS system is not easy to use: The scale hinders simple queries: The IMTS data are of variable quality: 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: 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, HS codes that are identifiable as raw materials or relatively undifferentiated intermediate products, and grouping them by resource type.

The CHTRD employs a five-tier resource taxonomy permitting queries to be as atomised or aggregated as required. The Chatham House Resource Trade Database employs a systematic approach to identify and manage data gaps and errors.

The CHRTD is 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, However, it exploits 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. 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 merit.

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 mass 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 mass of the trade some reports contain only the value , we perform a distributional analysis of the value-to-mass ratio for all trades of the given commodity in the given year, i.

This results in approximately 0. This is illustrated in the below figure, which plots the 17 distributions for live sheep unit prices for Not all commodity-year distributions have a lognormal structure however; some exhibit other structural properties that need to be accounted for. We therefore algorithmically identify whether secondary structures are present in the distribution and whether outlying data points require deflagging as outliers.

The number of bins is defined by the Freedman-Diaconis rule. 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.

The outcome of this process is illustrated in the below figure: 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 deflagged 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 are manually checked by researchers to ensure there is a valid rationale for their identification e.

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 the relative distances from world average unit prices. Data points that are deemed unreliable and irreconcilable are labelled as such and quarantined. Embodied carbon dioxide volumes are calculated by multiplying trade volumes by product-level carbon intensity factors.

The emission factors employed are from Sato Sato presents a detailed discussion, and sensitivity test, of the advantages and disadvantages of using world average factors relative to country-specific factors. This contrasts with alternative system boundaries such as gate-to-gate, cradle-to-grave including the use and disposal phases of the product and cradle-to-cradle including recycling. The cradle-to-gate carbon intensity factors assume that all production inputs are sourced domestically, i.

This system boundary results in issues of double-counting emissions at aggregated levels, since emissions associated with relatively unprocessed materials will also be recorded against products that have the original materials as inputs.

For aggregate national emissions inventories, double-counting is more of an issue for countries with significant trade volumes relative to the size of their economy, and for countries engaged in significant processing, with large import contents in their exports. This cradle-to-gate approach, however, is well-suited for comparing trade-adjusted emission inventories at a more detailed product-level.

As such, to avoid double-counting and over-representing emissions, we report embodied CO 2 emissions only at individual product level rather than for categories of aggregation. Embodied land area and water volumes are calculated for a sub-set of agricultural products by the Global Landscapes Initiative of the Institute on the Environment at the University of Minnesota, building on analysis they previously published MacDonald et al.

This is a refinement of the approach developed by Kastner et al. As such it is only possible to employ this methodology for commodities with a caloric value. CHRTD trade data for replace FAO trade data as the input trade data, other input values, for example caloric equivalence factors, production volumes, area harvested, and water productivity are derived from the same sources specified in MacDonald et al. Currently this site displays only embodied blue water values; we also have values for embodied green water, which we hope to add to the site in a future revision.

Please contact us for further details. The analysis using the CHRTD is innovative as it produces estimates of embodied resource volumes on each bilateral product flow, as opposed to previous estimates that consider the resources embodied on the overarching transfer of root crops between country of origin and the target country of final consumption.

This is also considered in the current analysis but as it is not compatible with displaying direct bilateral trade flows, it is not currently included on this site. Overviews of the key assumptions and decisions taken in developing this approach, and of the post-processing procedure used to derive values for bilateral product flows from the existing MacDonald et al.

The embodied land and water volumes reported are those associated with producing the root crop from which the traded commodity is derived, not with further processing stages. For calculation of re-exported crops there is no differentiation between imported and domestically-produced crop products.

Trades that have more than one intermediary between source and target can be neglected. This assumption is consistent with the MacDonald et al. All commodities from a given root crop are fungible. For example, if the US exports soy cake to the UK and the UK only exports soybeans to France, the soy cake is considered part of the total soy volume that can be exported from the UK. In other words, there is no tracking of which commodities can be converted into which other commodities.

The post-processing procedure is a method for estimating the properties underlying the re-export corrected trades. Each re-export corrected trade as reported using the procedure outlined in MacDonald et al. The post-processing procedure works as follows for each root-crop trade in the re-export corrected matrix:.

National environmental, socio-economic, governance, and resource-dependence indicators contextualize the significance of resource trade. Selecting a country name on the trade map gives you the option to display a country profile.

Product level embodied carbon flows in bilateral trade. Ecological Economics , , — BioScience , 65 3 , Embodied green and blue water: Greenhouse gas emissions per capita: Yale University Environmental Performance Index. World Bank World Development Indicators. Population with access to electricity: Transparency International Corruption Perceptions Index.

World Bank Worldwide Governance Indicators. Quality of overall infrastructure: Chatham House Resource Trade Database.

Asian Infrastructure Investment Bank. Agricultural Market Information System. Association of Southeast Asian Nations. Community of Sahel-Saharan States.

Common Market for Eastern and Southern Africa. Economic Community of Central African States. Economic Community of West African States. Extractive Industries Transparency Initiative - candidate, compliant, suspended. Fragile and conflict affected situations.

Cooperation Council for the Arab States of the Gulf.