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Workshop on Material Flows Accounting in Philippines



Expert Group Meeting

Meeting Title: Workshop on Material Flows Accounting in Philippines

Location: Manila, Philippines

Date: 20th - 22nd November 2017

Partners: IRP

SDG Indicators: 8.4.1, 8.4.2, 12.1.1, 12.2.1, 12.2.2

Type: Material Flow Accounts

Brief Description:

Environmental pressures and impacts are unintended consequences of modern systems of production and consumption. National material flow accounts and indicators deliver a very comprehensive overview of natural resource extraction, trade in natural resources, waste disposal and emissions. They measure environmental pressures of natural resource use and material flow accounting (MFA) headline indicators have been used as a proxy for overall environmental pressure and impact of a national economy. For this reason, indicators based on MFA data sets have been adopted for monitoring progress of the 2030 sustainable development agenda and the SDG targets for resource productivity (SDG 8.4) and sustainable use of natural resources (SDG 12.2).

UN Environment conducted a Workshop on Material Flows Accounting in Philippines. This was done in close collaboration with IRP, CSIRO, Nagoya University, University of Vienna and Institute of Social Ecology. The country workshop targeted National Statistical Offices and the Ministry of Environment.

The overall aim of the training is to establish a preliminary material flow account for the Philippines and to collect feedback about the implementation and practicability of the draft Global MFA Manual in the context of the Philippines ONS. Un Environment detailed introduction on concepts and methods of Material Flow Accounts as well as indicators derived and potential applications.

The workshop tested the then current draft of a new Global Material Flow Accounting Manual and got feedback on it. The participants familiarized themselves with Material Flow Accounting. They were able to analyze available data in the Philippines, as well as understand how to identify and fill in the data gaps.