Data Crunch: How do we measure the complicated relationship between poverty and the environment?

Over the last 10 years, countries in the Asia-Pacific region, that are part of the global UN effort – Poverty-Environment Initiative (PEI) have been working with UNDP and UN Environment to promote sustainable development for achieving poverty eradication and environmental conservation. PEI-supported countries have made a notable progress in integrating pro-poor environmental priorities into the national, sectoral and sub-national development planning and budgeting processes. However, many of the countries in the region have been experiencing challenges in measuring and accounting for the nexus between poverty and environment in an empirical and systematic manner.

Existing frameworks like the SEEA (System of Environmental-Economic Accounting) while quantifying the links between the environment and socio-economic well-being, still contain gaps. In order to address this and build on existing knowledge, PEI Asia-Pacific is developing and piloting the application of the Poverty-Environment Accounting Framework- the PEAF.

The Poverty-Environment Accounting Framework is an adaptation of the SEEA that can be used to generate information that responds to specific poverty-environment-nexus indicators. Integrated data will help better demonstrate the causal relationships between environment and poverty and further help in quantifying, monitoring and reporting on these relationships to inform policymaking and investment decisions.

This pilot Poverty-Environment Accounting Framework (PEAF) was presented at UNESCAP’s Sub-Regional Workshop on Environmental Statistics for ASEAN Countries held on the 4th of November.  National Statistics Officers from key countries that work with PEI like Lao PDR, Indonesia, Myanmar and Philippines were in attendance, in addition to countries such as Malaysia, Cambodia and Timor Leste, who were introduced to both the PEAF as well as PEI’s approach.

Following this, the Poverty-Environment Accounting Framework was also presented at the “Expert Group Training Workshop on the situation analysis of environmental statistics for developing Bangladesh Environmental Statistics Framework 2016-2030” held on the 6th of November in Dhaka by the Bangladesh Bureau of Statistics. PEI is supporting the Statitsics Bureau and the Planning Commission to better link poverty data to environment/climate/disaster data to inform poverty reduction policies and programmes by applying the Poverty Environment Accounting Framework. This will directly inform the inter-sectoral monitoring framework of the Sustainable Development Goals and Bangladesh’s 7th Five Year Plan. Speaking about the usefulness of the workshop and the accounting framework, Mr. Rafiq-ul-Islam (Bangladesh Bureau of Statistics)  said, “ The Poverty Accounting Framework is very relevant to Bangladesh right now. In November we held an Expert Group Meeting that was attended by 65 participants from 28 different ministries. Thanks to the UNDP and UN Environment, the PEAF is important for Environmental Statistics Framework for Bangladesh as well as for collecting data for policy making for the SDGs and to meet the targets of the 7th Five Year Plan. “

Would you like to learn more about the Poverty-Environment Accounting Framework? Click below on the link to the current draft of the Poverty-Environment Accounting Framework, click here for presentations and watch the video below where Mark Eigenraam, advising the PEI Asia-Pacific explains why the PEAF matters and how it can be applied for the Sustainable Development Goals. Also, hear from Rafiq-ul-Islam of the Bangladesh Bureau of Statistics on the usefulness of the Poverty-Environment Accounting Framework to Bangladesh’s work on developing a framework for environmental statistics. 

Poverty-Environment Accounting Framework: Working Paper

For questions and to know more about our work on the PEAF, please contact Seonmi Choi at [email protected] and Mark Eigenraam at [email protected]

 

 

 

 

 

Country Reference: 
Bangladesh
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