CRDM Primer

Climate Relevant Data Management An Introduction on Designing Systems for Data Collection, Storage & Retrieval

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Published by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH Philippine Climate Change Commission (CCC)

Project

http://mitigationpartnership.net/information-matters

Registered offices Bonn and Eschborn, Germany T +49 228 44 60-0 (Bonn) T +49 61 96 79-0 (Eschborn) Friedrich-Ebert-Allee 40 53113 Bonn, Germany T +49 228 44 60-0 F +49 228 44 60-17 66 Dag-Hammarskjöld-Weg 1-5 65760 Eschborn, Germany T +49 61 96 79-0 F +49 61 96 79-11 15 E: info@giz.de I: www.giz.de

Authors Mary Martha Merilo Sandee Recabar Voltaire Acosta Mara Mendoza

Inputs from Ricardo Energy and Environment (formerly Ricardo-AEA)

Copyright Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH Philippine Climate Change Commission

Place and date of publication Manila, Philippines 2017

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Climate Relevant Data Management An Introduction on Designing Systems for Data Collection, Storage & Retrieval

A PUBLICATION BY THE DEUTSCHE GESELLSCHAFT FÃœR INTERNATIONALE ZUSAMMENARBEIT (GIZ) GMBH AND THE PHILIPPINE CLIMATE CHANGE COMMISSION (CCC)

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Table of Contents List of Tables......................................................................................................................................................................iv List of Figures...................................................................................................................................................................iv Introduction..........................................................................................................................................................................1 Uses of Climate-relevant Data in the Philippines......................................................................................2 Collection and Management of Data....................................................................................................................3 Pillars of Data Management.............................................................................................................................3 Processes and procedures for data collection and management............................................6 Access to Data...................................................................................................................................................................7 Data Management Solutions and Storage of Data......................................................................................8 Roadmap.......................................................................................................................................................................8 Requirements.............................................................................................................................................................8 Technology....................................................................................................................................................................9 Data Management and Governance..........................................................................................................10 QA/QC of data..................................................................................................................................................................11 Elements of a QA/QC system.......................................................................................................................12 QA/QC Plan......................................................................................................................................................13 QA/QC Implementation.............................................................................................................................13 Documentation and Archiving.............................................................................................................13 Application of QA/QC.........................................................................................................................................14 GHG inventory: national and sectoral............................................................................................14 NAMAs................................................................................................................................................................15 Climate finance.............................................................................................................................................17 Endnotes..............................................................................................................................................................................19

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List of Tables Table Table Table

Table Table Table Table

1. GHG inventory sectoral “data keepers” – formats of data...........................................4 2. Key data considerations......................................................................................................................5 3. Examples of available data per sector (based on previous Training-Workshops on MRV and Baselines Scenario Setting, April 24-25 & 28-30, 2014)..............................................................................................................6 4. Non-functional Requirement headers.........................................................................................9 5. Functional requirement capabilities............................................................................................9 6. IPCC - TCCCA Principles...................................................................................................................11 7. CDM Principles for QA/QC...............................................................................................................11

List of Figures Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

Figure Figure Figure Figure Figure

1. Coordination and MRV...........................................................................................................................2 2. Typical Institutions in an MRV System......................................................................................3 3. Typical Institutions in an MRV System......................................................................................4 4. Road map to a data management solution............................................................................8 5. Data governance cycle......................................................................................................................10 6. Sample data management solution system.........................................................................10 7. Relevance of quality in climate actions................................................................................12 8. Elements of a QA/QC system........................................................................................................12 9. Practical considerations in developing QA/QC system................................................13 10. QA/QC in GHG Inventory Cycle.....................................................................................................14 11. 2006 IPCC Guidelines for National Greenhouse Gas Inventories - Checklist for General QC checks............................................................................................15 12. Data flow from NAMAs......................................................................................................................16 13. Process in assessing the impact from a NAMA/policy................................................16 14. CDM Guidelines on QA/QC...............................................................................................................17 15. QA/QC of Climate Finance...............................................................................................................18 16. Data Providers on Climate Finance - Roles and Responsibilities........................18

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Introduction

I

n developing mitigation actions to address the adverse effects of climate change, government agencies and sectoral stakeholders often face the challenge of generating climate-relevant data. These information are needed to design appropriate mitigation actions and policies in the planning stage. The availability of these data is likewise crucial in monitoring progress and reporting outputs and outcomes in the results stage.

As the Philippines works toward low carbon development and the sustainable development goals, while at the same time meeting international climate change agreements, some forms of reporting have to be fulfilled. These reporting requirements, which come with standards and templates, provide further guidance and direction in data collection and management. Thus, a data management system will serve as a useful tool to expedite data processing for various reportorial requirements in the national and international levels. At the same time, such system would provide inputs for other development objectives especially in aid of national or sectoral planning and policy formulation in relation to climate change mitigation.

This Primer on climate-relevant data management aims to guide relevant sectors and stakeholders in the collection, management, processing, and storage of data related to climate change.

The Philippine Climate Change Commission (CCC) and the Deutsche Gesselschaft fur Internationale Zussamenarbeit (GIZ) jointly developed this Primer based on the presentations and outputs of Ricardo Energy and the Environment (formerly Ricardo AEA) which was commissioned to conduct capacity building activities and provide technical support for the project Information Matters: Capacity Building for Enhanced Reporting and Facilitation of International Mutual Learning through Peer-to-Peer Exchange (Information Matters Project).

Supported by the International Climate Initiative of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB), the Information Matters Project aims to build the institutional capacity of the Philippine government through the CCC on international climate reporting especially in the preparation of National Communications (NCs) and Biennial Update Reports (BURs) in line with the country’s commitment to the United Nations Framework Convention on Climate Change (UNFCCC) as Party to the Convention.

The Project covered four countries, namely: Chile, Dominican Republic, Ghana, and the Philippines through the support and cooperation of the United Nations Development Program – Low Emission Capacity Building Program (UNDP-LECB), National Communication Support Program (NCSP), United Nations Environment Programme (UNEP), World Resource Institute (WRI), and the International Partnership on Mitigation and MRV (IMMA). Activities undertaken under the Project include Gap Analyses, Capacity Building Missions, Backstopping and Technical Support, and Peer-to-Peer Exchanges.

For more information on the Project implementation in the Philippines: http://climate.gov.ph/ convergence/mitigation/information-matters-philippines For further information on the global implementation of the Project and to access different tools and knowledge products under the Project: http://mitigationpartnership.net/information-matters 1

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Climate Relevant Data Management

Uses of Climate-relevant Data in the Philippines Climate-relevant data management supports the identification of the sources of Greenhouse (GHG) emissions, the setting of emission reduction contributions or targets, and the monitoring and tracking of progress of climate change mitigation actions.

The GHG inventory is an example of climate-relevant data. The inventory provides countries baseline information to identify and prioritize sectors for emission reduction planning activities and identify corresponding financial resources to implement these activities.

Thereafter, emission reduction will be measured, reported, and verified, allowing the country to evaluate the outcomes of the activities and modify the activities or targets as necessary.

Climate-relevant information needed to determine progress in mitigating climate change could be obtained from other data collection mechanisms, which are not necessarily designed for climate tracking. For instance, information on the number of registered vehicles and their fuel consumption are being obtained as part of the transportation sector database but such could also serve as activity data in estimating vehicular emissions and in identifying its mitigation potential. Thus, a system of data management should be in place to ensure regular data flow and continuous coordination among data providers and database keepers.

Figure 1 shows the institutions involved in climate relevant data management in the Philippines. In the country, the CCC is responsible for coordinating with the key agencies their available data, data requirements, technological requirements, and their recommended access authorities.

CCC (Central Coordinator)

PSA

Data Team and Members of Steering Committee Department of Agriculture (DA) / Philippine Statistics Authority (PSA)

Department of Energy

Department of Environment and Natural Resources – Environmental Management Bureau (DENR-EMB)

DENR – Forest Management Bureau (DENR-FMB)

Department of Transportation (DOTr)

Agriculture

Energy

Waste and IPPU

Forestry

Transportation

• CCC as overall/central coordinating agency • Sector Agencies: To handle sectoral MRV with corresponding Data Team for Database Management • Steering Committee: To discuss MRV related issues, spearheaded by the CCC with members from each sector

Figure 1. Coordination and MRV Source: Workshop Output, Information Matters Project Training-Workshop on MRV Domestic Architecture and Baselines Scenario Setting, Manila, Philippines, April 24-25 and 28-30, 2014

It is also important to note of the sustainable development benefits i in collecting, processing, and reporting climate-relevant data which can provide inputs in addressing other issues such as health, biodiversity, and public policy development. On a wider scale, climate-relevant data can contribute to the design and achievement of national green growth targets.

Given the many climate-relevant data sources from different providers in varying scopes, formats, dates of collection, and levels of detail, the challenge is to identify the extent of climate-relevant data i to be captured and inputted into the system.

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Climate Relevant Data Management

Collection and Management of Data Designing a sustainable system of collection and management of data entails a review of the key elements of measurement, reporting, and verification (MRV) since data collection and management are included in the wider MRV system. This can be further observed by creating the enabling environment for regular data collection and management. Whether on a project or activity level, sectoral in scale or national policy in scope, it is important to identify the sources, keepers, or managers of data as well as institutional roles and their responsibilities (Figure 2).

PILLARS OF DATA MANAGEMENT To sustain the availability of data, there are certain conditions that needs to be met: implementation of policies or directives (legal); creation of necessary structures, identifying who would be involved and their roles, strategy-making, and capacity building (institutional); and establishing tools and guidelines for data collection, storage, and access (procedural) (Figure 3). At present, sectoral government agencies in the Philippines are conducting GHG inventories of their respective sectors following a specific format as shown in Table 1. .

Central MRV team

• Overall responsibility for MRV system • Manage implementation of the MRV system

Data team

• Manage database (if needed) • Coordinate data agreements

Steering committee

• A forum for discussing over-arching issues such as roles and responsibilities for data sharing, overlaps between response measures, proposed methodologies for data calculation etc.

Data providers Coordinating departments

• Provide data on impacts on the ground - could be national government department, regional government, business etc.

• Play a key role in coordinating data collection for a particular sector.

For example if there are two or more waste sector NAMAs in a country, the coordinating department might oversee data methodologies and collection

Figure 2. Typical Institutions in an MRV System Source: John Watterson and Yvonne Pang, “Collection and management of data” (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014

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Climate Relevant Data Management

SYSTEM ELEMENT

BUILDING BLOCK • Laws and regulations • Compliance • Enforcement and corrective action

Legal

• Which institutions? Ministries, departments and agencies (MDAs) • Roles and responsibilities • Coordination and cooperation • Capacity and skills and training

Institutional Procedural This includes all procedures, processes and systems

• Tools and guidlines • Indicator definition and monitoring • Baseline setting: projections • Data and information collection • Analysis and quality control

• Verification (internal and external) • Reporting and dissemination • Continuous improvement • Stakeholder engagement

Figure 3. Elements and Building Blocks of MRV Source: John Watterson and Yvonne Pang, “Collection and management of data” (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014)

Table 1. GHG inventory sectoral “data keepers” – formats of data SECTOR

FORMATS OF DATA

ENERGY

Microsoft Excel Files - consolidated by the Energy and Policy Planning Bureau of the Department of Energy (DOE - EPPB)

TRANSPORT

Oracle Software –consolidates transportation data

AGRICULTURE

Microsoft Excel Files – statistical sampling ALU Software – for greenhouse gas inventory which will be input into the Intergovernmental Panel on Climate Change (IPCC) Software

WASTE

Interactive Database System (DBMS) – platform for Department of Environment and Natural Resources’ Environment and Management Bureau (DENR-EMB) regional offices to update information regularly Hard copies – another format submitted by regional offices which the Management Information System of the National Solid Waste Management Commission Secretariat shall encode into the DBMS

INDUSTRY

Self-monitoring Reports/Compliance Monitoring Reports - Submitted in hard copies to EMB but pending extraction of climate mitigation relevant data Annual Reports - Submitted by industry associations in hard copies to EMB who shall then extract data (e.g. cement, steel) and input into the IPCC software

Forestry

Geographic Information System (GIS) models Random Field Verification

Source: John Watterson, Yvonne Pang, “Uses of climate relevant data in the Philippines” (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014)

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Climate Relevant Data Management

DATA CONSIDERATIONS There are a number of considerations in looking at data availability and data requirements.

As presented in Table 2, these considerations include the scale of data, data sources, types of data, frequency of update, and time series and quality. Table 3, on the other hand, identifies the available data for the various sectors in the country.

Table 2. Key data considerations KEY DATA CONSIDERATIONS SCALE OF DATA

• A very wide range of data could be needed – and could be part of the National Integrated Climate Change Database Information and Exchange System (NICCDIES) • Even a simple spreadsheet could contain thousands of numbers • As an example, the United Kingdom’s National Atmospheric Emissions Inventory (NAEI) has more than 107 data points in the main database

FREQUENCY OF UPDATING

TIME SERIES AND QUALITY

• Reporting obligations can dictate • Non-spatial, frequency (e.g. and spatial BURs)

• This can be a problem – older data sets may not exist, or of poor quality

SOURCES OF DATA

TYPES OF DATA

• A wide range - from government agencies, Civil Society Organizations (CSOs), NonGovernment Organizations (NGOs), including academia

• Numeric, and textual

• Scales of international, national, local

• Wide range • May range from • Interruptions to of electronic hourly (e.g. data availability formats meteorological - raises issues data) to every of time series several years consistency (infrequent surveys or • The quality of censuses) data collected in the future can be controlled • Long time series of data can soon accumulate – providing large volumes of data

Source: John Watterson and Yvonne Pang, “Collection and management of data” (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014)

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Climate Relevant Data Management

Table 3. Examples of available data per sector (based on previous Training-Workshops on MRV and Baselines Scenario Setting, April 24-25 & 28-30, 2014) AVAILABLE DATA PER SECTOR FORESTRY SECTOR

WASTE SECTOR

INDUSTRY SECTOR

Forest Cover: National Forest Inventory updated every 5 years

Volume of Waste Generated in tons

Number of industries

No of LGUs with SWM plans and Waste Analysis and Characterization Study

Type of industries

Forest Cover Change Analysis

Biological Oxygen Demand EMB

No of LGUs complying to RA 9003 No of open dumpsites closed

Timber Demand Projections

Data production for some industries (EMB)

No of LGUs with MRF

Forest Resources Assessment

No of Sanitary Landfill Constructed and rehabilitated

Rate of Deforestation

Rapid data Assessment

Data Materials consumed

Population Stats Best Available technologies ENERGY

TRANSPORT

Production and Consumption of Fuel by type and sector including: Import, Export, Transformation and Generation

Franchise data for buses (LTFRB) IPCC Emission factor Type of fuel that car be used per bus

Source: John Watterson and Yvonne Pang, “Collection and management of data� (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014)

PROCESSES AND PROCEDURES FOR DATA COLLECTION AND MANAGEMENT There are number of approaches in collecting data from various sources including online databases and portals, national statistics, surveys and censuses, measurements, literature review and research, and meetings and discussions i. Equally important are the mechanisms by which these approaches can be undertaken, depending on the preferred or necessary degree of ease of access or level of formality. These can be done through some form of informal or formal agreement among agencies and stakeholders or through legal agreements or fiduciary standards which bind data providers to report or enable access to their data.

However, it should also be noted that as ease of data collection increases, the level formality of the mechanism for collection decreases.

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Climate Relevant Data Management

Access to Data Identifying the users of data helps in designing the data management system as there are different functionalities ii for different types of users, especially when in terms of accessing data. This is especially important for sensitive data or information which may compromise private entities or agencies. Moreover, users may have different objectives for accessing data and these require varying levels of detail and information generation processes. As such, access policies and rights i can only be granted to specific types of users.

In addition, it also helps to consider the barriers for accessing and sharing data i. These barriers could be related to the hardware wherein certain specifications for computers and other devices may not be available to the user or provider; or from the software wherein certain types of programs or operating systems and updates are necessary to run the database and maximize its functionalities. Aside from technology barriers, apprehension from key sources or providers to provide necessary data can also hinder access, especially when data needed contain sensitive information that may compromise their organizations or operations. As such, a system or process for the management of sensitive data i should be in place as well as agreements or policies for sharing and accessing data i to ensure its responsible and fair use. In accessing data, it is also important to build trusts and confidence between data users and providers.

Lastly, considering possible risks i may also enhance the reliability of the data management system. These risks may involve security issues such as hacking or hijacking web-based databases, and the intrusion of spywares or malwares, which may affect operating systems and the database itself. Other risks may be related to financial aspects especially when funds for the operation of the data management system come from non-permanent fixtures, or when the costs for operations are too expensive and may not be sustainable in the long run.

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Climate Relevant Data Management

Data Management Solutions and Storage of Data Providing solutions to data management and data storage result to a data management platform (DMP). Also known as a unified data management platform (UDMP), DMP is a centralized computing system for collecting, integrating, and managing large sets of structured and unstructured data from disparate sources iii.

ROADMAP Coming up with technical solutions for data management usually takes four steps, also known as the “Agile” framework. These steps as presented in Figure 4 include ‘discovery’, which pertains to establishing and understanding user requirements i; ‘alpha’, which entails exploring user requirements i; ‘beta’, which involves testing and developing user requirements i; and, ‘live’, or delivering user requirements i.

DISCOVERY • Establish responsibility • Understand User Needs • Develop requirements • Prioritised list of user stories to explore • Simple mock-ups & Rough Prototypes • Identify Stakeholder • Understand existing services • Start developing KPIs and targets • Outputs

ALPHA • Develop further user stories and Requirements • Form your team • Test technologies • Test design approach • Is the design and approach: • Appropriate • Viable • User centric

• Outputs:

BETA • Working prototype • Iterative testing of the prototype with users • Continuous improvement from iterative testing • Outputs • End to end prototype for approval • User testing plan • KPIs • Working system

LIVE • Iterative improvements • Don’t stand still

• Monitor performance • Optimise • Maintain the service • User feedback • Cost effective

• User stories • Requirements Tracker • Beta plan

• Set of Requirements

Figure 4. Road map to a data management solution Source: James Jaggers, John Watterson and Yvonne Pang, “Data management solutions and Storage of data” (presentation, Information Matters Project Training Workshop on Climate Relevant Data Management, Manila, Philippines, August 11-13, 2014)

REQUIREMENTS There are two types of requirements in data management: functional and non-functional requirements. The non-functional requirements facilitate the delivery of the intended functions of the data management platform, thus it is more involved with the operation of the system rather than the functionality i. The functional requirements, on the other hand, produce the actual intended functions of the data management platform, and thus defines the activities, functionality, and behaviour of the system i. The various non-functional and functional requirements of a DMP are listed in Table 4 and Table 5 below.

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Deutsche Gesellschaft fĂźr Internationale Zusammenarbeit (GIZ) GmbH Registered offices Bonn and Eschborn, Germany 9th Floor PDCP Bank Centre, Rufino corner Leviste Streets, Salcedo Village, 1227 Makati City, Philippines Contact Dr. Bjoern Surborg Principal Advisor Support to the Philippines in Shaping and Implementing the International Climate Regime (SupportCCC II) Project Tel. +63 2 426 0726 Fax +62 2 426 0726 Email: bjoern.surborg@giz.de www.giz.de

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