Data-driven evidence to inform decision-making on climate and governance

Innovation methods

Data sources

SDG(s)

Sustainable Development Goal(s)

10Reduced innequalities

13Climate action

17Partnerships for the goals

Disclaimer:

Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.

Title

Please provide a name for your action learning plan.

Data-driven evidence to inform decision-making on climate and governance

Challenge statement

Challenge type: If you are working on multiple challenges, please indicate if this is your "big bet" or "exploratory" challenge. Please note: we ask you to only submit a maximum of 3 challenges - 1x Big Bet, 2x Exploratory. Each challenge must be submitted individually.

EXPLORATORY

Challenge statement: What is your challenge? (Please answer in specific terms: "Our challenge is that...”.)

Our challenge is that there are non-existent or sub-optimal practices at municipal level that can block moving towards better practices on circular economy, water management and citizen participation.

Background: What is the history of your challenge? What is causing or driving it? Who is involved? How does the current situation look like? What undesired effects does it produce?

There is a lack of frequent and municipal-level information that creates obstacles for local and central governments in designing timely and appropriate interventions that benefit the most vulnerable. There is also a multi-dimensional complexity associated to climate challenge (e.g. solid waste and water scarcity), and governance challenges (e.g. citizen participation).

Quantitative evidence: What (official) data sources do you have on this challenge that better exemplifies the importance and urgency of this frontier challenge? You can add text, a link, or a picture.

Multidimensional Poverty Index (MPI) 2018, UNDP Municipal Human Development Index (IDHM) 2018, UNDP Illegal and Municipal Dumpsites 2021, Ministry of Environment Departmental and Municipal Limits 2022, National Geographical Institute Access to the collection and burning and burial service 2018, National Statistics Institute Points of interest 2020, The Humanitarian Data Exchange (HDX), OpenStreetMap Polygons of populated places 2020, National Geographical Institute Hydrography 50K of Motagua River, Ministry of Environment Hydrographic limits 2022 of micro-basins level 8, Ministry of Environment Characterization of Solid Waste 2022, Ministry of Environment Biophysical Characteristics: Precipitation and Geology, National Institute of Meteorology and Hydrology Municipal Ranking 2021, Secretary of Planning and Programming of the Presidency

Proof of Concept: Integration and analysis of diverse data sources

Qualitative evidence: What weak signals have you recently spotted that characterizes its urgency? Please provide qualitative information that better exemplifies the importance and urgency of this frontier challenge. You can add text, a link, or a picture.

Value proposition: What added value or unique value proposition is your Accelerator Lab bringing to solving this challenge? Why is it your Lab that needs to work on this challenge and not other actors within UNDP, other stakeholders in the country respectively? Why is it worth investing resources to this challenge?

By generating and using data-driven evidence key decision-makers can promote the adoption of better practices on climate and governance that would ultimately benefit the most vulnerable

Short “tweet” summary: We would like to tweet what you are working on, can you summarize your challenge in a maximum of 280 characters?

Data-driven decision-making models combine multiple sources of information to enable local and central government to design better interventions on climate and governance at municipal-level that benefit the most vulnerable

Challenge classification

Partners

Who are your top 5 partners for this challenge? Please submit from MOST to LEAST important and state Name, Sector and a brief description of the (intended) collaboration.

Please state the name of the Parter:

Ministry of Environment

What sector does our partner belong to?

Government (&related)

Please provide a brief description of the collaboration.

Currently collaborating with three teams within the Ministry of Environment in integrating relevant data sources and performing analysis that can guide their decisions. Such teams are (1) National Information System for Climate Change, (2) Directorate for the Management of Residues and Solid Waste, and (3) Motagua River Project -- the river face a severe pollution creating a geopolitical crisis with the neighboring country Honduras

Is this a new and unusual partner for UNDP?

Who are your top 5 partners for this challenge? Please submit from MOST to LEAST important and state Name, Sector and a brief description of the (intended) collaboration.

Please state the name of the Parter:

UNDP GIS team (HQ/BMS/ITM)

What sector does our partner belong to?

United Nations

Please provide a brief description of the collaboration.

Guiding and supporting geographical data analysis tasks, such as building a proof of concept for guiding municipal-level interventions in the Motagua River Basin based on different data layers related to human development, waste and natural resources.

Is this a new and unusual partner for UNDP?

Who are your top 5 partners for this challenge? Please submit from MOST to LEAST important and state Name, Sector and a brief description of the (intended) collaboration.

Please state the name of the Parter:

Humanitarian OpenStreetMap (HOT)

What sector does our partner belong to?

Civil Society

Please provide a brief description of the collaboration.

Co-design of an open mapping instrument for collecting citizen data on households aspects of waste management. This to generate further evidence that could drive the decision making of relevant authorities and involving citizens and volunteers.

Is this a new and unusual partner for UNDP?

Yes

Learning questions

Learning question: What is your learning question for this challenge? What do you need to know or understand to work on your challenge statement?

What information layers and data analysis methods are useful for guiding municipal-level interventions of key decision makers?

To what stage(s) in the learning cycle does your learning question relate?

Explore, Test, Grow

Usage of methods: Relating to your choice above, how will you use your methods & tools for this learning question? What value do these add in answering your learning question?

The tools and methods allow to collect and perform analysis on large size databases and at higher geographical resolution that would otherwise be difficult to perform manually

Existing data gaps: Relating to your choice above, what existing gaps in data or information do these new sources of data addressing? What value do these add in answering your learning question?

Identified data gaps include more frequent and higher geographical resolution insights that can complement official statistics, where traditional data collection mechanisms in place happen sporadically and are prone to errors. A key challenge with the new data sources is to ensure its validity.

Closing

Early leads to grow: Think about the possible grow phase for this challenge - who might benefit from your work on this challenge or who might be the champions in your country that you should inform or collaborate with early on to help you grow this challenge?

The Ministry of Environment and Natural Resources, international organizations and universities / researchers

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Innovation methods

Data sources

SDG(s)

Sustainable Development Goal(s)

10Reduced innequalities

13Climate action

17Partnerships for the goals

Action plan