FDES: Chapter 1. Overview of Environment Statistics Characteristics and Challenges. FDES: Framework for the Development of Environment Statistics

Transcription

1 FDES: Chapter 1 Overview of Environment Statistics Characteristics and Challenges FDES: Framework for the Development of Environment Statistics Environment Environmental Protection, Resources and Management and their Use Engagement 1. Environmental Conditions and Human Habitat Emissions, Quality and Residuals and Environmental Waste Health 4. Disasters and Extreme Events

2 Overall programme FDES Chapters (90min each) Module Title 1 Chapter 1: Overview of Environment Statistics Characteristics and Challenges 2 Chapter 2: Conceptual Foundation and Structure Chapter 5: Applications of the FDES to Cross-cutting Environmental Issues Chapter 3: Components of the FDES and the Basic Set of Environment Statistics Environmental Conditions and Quality Environmental Resources and their Use Residuals Extreme Events and Disasters Human Settlements and Environmental Health Environmental Protection, Management and Engagement 9 Chapter 4: From the Basic Set to the Core Set of Environment Statistics 2

3 Outline Level 1 (20 min) 1. Objective, Scope, Users 2. Information, data, statistics and indicators 3. Data sources Group exercise (30 min) Level 2 (30 min) 4. Classifications 5. Time, Space 6. Institutional dimension Group discussion (10 min) Environment Environmental Protection, Resources and Management and their Use Engagement 1. Environmental Conditions and Human Habitat Emissions, Quality and Residuals and Environmental Waste Health 4. Disasters and Extreme Events 3

4 Learning objectives Level 1 Be able to use the FDES manual Be familiar with the characteristics and challenges in environment statistics Learn to describe your data Level 2 Understand data sources, main classifications, spatial and temporal dimensions Learn about institutional challenges (and what to do about it) 4

5 Why environment statistics? Improve knowledge Support evidence-based policy Provide information to the general public, media and user groups 5

6 Environment statistics are interdisciplinary and inter-institutional Environment statistics are about: 1. the state of the environment, 2. our dependence on it, 3. our impact on it, 4, 5. it s impact on us (even negative ones), and 6. how we protect and manage it. This information comes from many institutions using different methods, concepts and classifications. FDES: Framework for the Development of Environment Statistics 6. Environment Protection, Management and Engagement 5. Human Habitat and Environmental Health 1. Environmental Conditions and Quality 4. Disasters and Extreme Events 2. Environmental Resources and their Use 3. Emissions, Residuals and Waste 6 ESCAP - Environment Statistics Overview

7 Information, data, statistics, accounts and indicators Information The world Metadata Data: Qualitative Quantitative Rules: Structure, Aggregate Statistics Select Themes: Stock/flow Accounts Indicators Combine Indices 7 ESCAP - Environment Statistics Overview

8 A note on frameworks Quantity Price FDES: Framework for the Development of Environment Statistics Integrate statistics SEEA: System of Environmental- Economic Accounting SNA: System of National Accounts Value of nature 8

9 A note on frameworks FDES is an organizing framework - Based on Pressure-State-Response - Good for compiling basic statistics and reporting - Covers most issues of concern BUT: May risk viewing all human activities as pressures SEEA is an accounting framework - Integrates related statistics into accounts (e.g., water, energy, land, ecosystems) - Links to SNA by using same classifications and methods - Good for compiling integrated indicators (e.g., water intensity of economy) to assess trade-offs - BUT: Covers limited set of issues & less experience Note: The two can be used together. Can start with FDES to compile basic statistics. Can start with SEEA to identify accounts of interest. 9

10 Main users & uses Users Policy and decision makers General public (media, education, civil society) Analysts, researchers, academia Metadata Types of information Detailed data Accounts National statistical offices Indicators National agencies International agencies 10

11 The statistics supply chain Statisticians assess user needs, design & build collection systems, collect & compile, validate, describe and structure data to produce statistics series, accounts and indicators. This applies to all government departments, not only NSOs. Statistical activity 1 (Primary) Needs Design Build Collect Validate Process Analyse Output Evaluate Statistical activity 2 (Primary) Needs Design Build Collect Validate Process Analyse Output Evaluate Statistical activity 3 (Secondary) Needs Design Build Collect Validate Process Analyse Output Evaluate Publications, databases Statistics Accounts Indicators 11

12 FDES Structure 6. Environment Protection, Management and Engagement 2. Environmental Resources and their Use 5. Human Habitat and Environmental Health 1. Environmental Conditions and Quality 4. Disasters and Extreme Events 3. Emissions, Residuals and Waste FDES describes the scope and components of environment statistics; harmonizes data with common standards. To compile these, you need to exploit many different kinds of data Environment Environmental Protection, Resources and Management and their Use Engagement 1. Environmental Conditions and Human Habitat Emissions, Quality and Residuals and Environmental Waste Health 4. Disasters and Extreme Events 12

13 Types of data sources Statistical surveys Administrative records Remote sensing and mapping Environmental monitoring Scientific research and special projects Global & international sources 13

14 Statistical surveys Census: Collects data for the entire population of interest (all people, all businesses, all ecosystems) Sample survey: Collects data for a representative portion of the population (e.g., one in 5,000 people) Results are weighted to represent the population Environment surveys can be specialized (e.g., Households and the Environment, Environmental Protection Expenditures ) Environment data can be obtained from other surveys by adding questions (e.g., source of water on Census) 14

15 Administrative records Data needed for administration Less expensive, less response burden, coverage known Examples: Utility records water & sewer, electricity & gas bills Tax returns environmental taxes and subsidies Government expenditures environmental protection Limitations: Different concepts, definitions and classifications Restricted access Poor data quality if not validated (e.g., business names) 15

16 Remote sensing and mapping Satellite images, aerial photos, land survey Satellite images land cover, habitat types Aerial photos = more detailed crop type, animals Land survey topographic (elevation), hydrological (rivers, streams, wetlands), soil condition Thematic mapping = classified and validated forest, shrubland Combined in Geographic Information System (GIS) Validate, integrate (make coherent) OneMap 16

17 Environmental monitoring Observations from field monitoring (air, water, soil, weather, animals) Advantages: Documented methods, validated, time series, models improve data quality Limitations: Storm chasing = not random sample Similar data from different jurisdictions/studies difficult to integrate 17

18 Scientific research & special projects Examples: UK National Ecosystem Assessment, ecosystem services valuation studies, effects of X on Y, case studies, ) Advantages: In public domain (research papers, databases) Don t need to do it over again (e.g., no response burden) Can fill data gaps (e.g., benefits transfer, function transfer, other estimates) Limitations: Different concepts, methods and classifications No access to raw data, no metadata One-time or one location case study 18

19 Global sources Most data in international databases are derived from national statistics But Global remote sensing and spatial estimates Good starting point, but may not agree with national data Some international organizations model national data Check your data in international databases! 19

20 Metadata - two sides of the coin Priority setting, institutional setting, work planning Inventory of environment statistics Recommended use FDES ESSAT (Environment Statistics Self- Assessment Tool) ESSAT Part I Statistics Institutional mandates ESSAT Part II Statistics Documentation of national system for environment statistics Long-term planning SEEA Diagnostic Tool Accounts Data sources & users ESCAP Inventory Template Statistical processes (inputs, outputs) Workshop setting Coordination of working group to develop specific pilot accounts 20

21 Group exercise Groups of minutes to prepare Statistical activity 1 (Primary) Needs Design Build Collect Select ONE dataset or statistical activity that you use or produce [See Inventory Template] What is it s name? [1.1] Who produces it? [1.2] What type is it? (survey, administrative, monitoring ) [2.2.2] How do you ensure its quality? [3.1] What is the output (indicators, database, publication)? [5.10] How is it used to make policy/decisions? [1.6] Report: 2 minutes! Warning! We ll document this dataset further. Validate Process Analyse Output Evaluate Statistical activity 2 (Primary) Needs Design Build Collect Validate Process Analyse Output Evaluate Statistical activity 3 (Secondary) Needs Design Build Collect Validate Process Analyse Output Evaluate 21

22 Welcome to Level 2! A few more concepts Classifications Time and space Geospatial information Institutional dimensions Discussion of priorities 22

23 Classifications A classification is a way of grouping data: Age (young = 0-15, working age = 16-65, retirement = 66+) Industry: primary, manufacturing, service Land cover: forest, inland water, shrubland, grassland, wetland, coastal Best if Mutually exclusive: One unit can be classified in only one category Exhaustive: Covers all possibilities (e.g., all species) Hierarchical: Detailed levels (e.g., dense coniferous forest) add to higher levels (e.g., all forest) Include definitions of what to measure (and how) 23

24 Classifications Why classify? Aggregate (simplify) complex data by common properties Rules and definitions make sure everybody classifies the same way Integrate environment data (e.g., pollution generation of industry) with economic data (e.g., value added of industry) A common language Standard classifications are the norm Variants can be explained relative to the standard Coherence problems if everybody uses different classifications 24

25 Classifications No universal environmental classifications Would be useful to get everybody to read from the same page Emerging and sectoral classifications LCCS: FAO Land Cover Classification System UNFC: UN Framework Classification for Fossil Energy and Mineral Reserves and Resources ILO: Employment in Environmental Activities SEEA: Land Cover, Land Use, Natural Inputs, Emissions, Effluents, Solid Wastes CEA: Classification of Environmental Activities (expenditures) CEPA: Classification of Environmental Protection Activities Groupings not developed for statistical purposes CRED-EMDAT: Natural and technological disasters Protected areas (UNEP-WCMC, IUCN) Threatened species (UNEP-WCMC, IUCN) Source categories for GHGs (IPCC) CICES: Common International Classification of Ecosystem Services OECD PRTR (Pollutant Release and Transfer Registry) 25

26 Classifications Environment statistics use existing social and economic statistical classifications ISIC: International Standard Industrial Classification of All Economic Activities CPC: Central Product Classification ICD: International Classification of Diseases COFOG: Classification of Functions of Government (OECD) COFOG 05 based on CEPA See UNSD: What is an international classification? See Hancock (2013) PDF 26

27 Time Time concepts Periodicity (frequency): How often collected? Period of record: When series started & ended? Reference/Accounting period: Measures represent what duration? Time of recording: When was the event recorded? Aggregate annually to align with economic and social data Need rules (depends on what is measured) Hourly daily monthly annual decade eon Sum, max, min, range, average, median, interquartile range 27

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29 and time again How often to measure? Depends on dynamics of phenomena (minute, hourly, seasonal flood cycles, slow ecological processes) Seasonal: Temperature, fish stocks, ice cap, fires Annual averages obscure variation! Hourly: Smog, PM 10 concentrations, temperature, rain Daily averages wash out hourly min/max (Bordt & Rastan, 2015) Daily: max/min temperature, hours of sunshine Quarterly: GDP, GHGs (estimated from leading trends) 29

30 Space the final frontier Like Time, Space is important Depends on what is being measured Environment not evenly distributed (= heterogeneous) Temperature, rainfall, humidity Land cover, habitats, species (some move around!) Pollution, people, roads Natural resources, disaster events Need meaningful spatial units and georeferenced data! Drainage area (for water-related) Ecozone (for ecological), land cover units Sometimes need to slice and dice to administrative areas (See Bordt, 2015) 30

31 Space Geospatial information Location & characteristics of atmosphere, surface & sub-surface Visual display & analysis of data (hot spots, regional differences) Maps are easy to use and to understand GIS can overlay multiple datasets for deeper analysis, modelling, filling gaps Much easier if National Spatial Data Infrastructure (NSDI) exists: agreed boundaries, standards, sources 31

32 Space Integration in GIS Source: GAO (2004) 32

33 Space Frequent and detailed satellite data available Need specialized skills & equipment (share!) Cartography: producing meaningful maps Analytical: Detail OK if validating quality, comparing with other maps Better to focus on a message than displaying data Less detail, easier to interpret & compare over time 33

34 Institutional dimensions Legal framework: NSOs have mandate to produce official statistics Remember Fundamental Principles Policy departments mandates to promote sectoral policies Environment statistics mostly collected by policy departments (environment, natural resources, agriculture, fisheries, forestry, health ) Requires coordination and collaboration Common objectives impartial evidence better decisions 34

35 Institutional dimensions Constraints to cooperation NSO may have little influence over National Statistical System (NSS) Overlapping, competing mandates No data sharing (protected by law!) Diverse technical capacity, methods, classifications Possible solutions Clarify role of NSO for quality, coordination of NSS Agree on common standards for environment statistics Share data for statistical purposes Work together (working groups, joint projects, training!) Engage the NSO when you re designing the project! 35

36 Discussion What kinds of priority data sources in your country: Are missing? Need work (e.g., help from statisticians, experts)? What kinds of work (quality, estimation, integration, aggregation, presentation)? What are some of the constraints to doing this? (data, technical capacity, institutions) Volunteers? 36

37 Take home points Good statistics are cheaper than bad decisions. Environment statistics are interdisciplinary and interinstitutional Therefore needs a conceptual and organizing framework FDES is a useful organizing framework SEEA can integrate most FDES statistics into accounts Most environment statistics are collected by policy departments (not NSOs) Agreeing on a framework will support collaboration, integration and maximize use of data 37

38 References Bordt, M. & Rastan S The Role of National Agencies as Honest Brokers Between Science and Policy: Case Studies on Environmental Sustainability Indicators. Handbook of Clean Energy Systems. Wiley. Bordt, M Spatial units, scaling and aggregation. UNSD (PDF) FAO LCCS Land Cover Classification System FDES: Government Accountability Office Geospatial Information: Better Coordination Needed to Identify and Reduce Duplicative Investments. Hancock, A. (2013). Best Practice Guidelines for Developing International Statistical Classifications (PDF) (No. ESA/STAT/AC.267/5). New York, NY: United Nations Statistics Division. OECD PRTR (Pollutant release and transfer registry) SEEA-CF: Statistics Canada Human activity and the environment 2013: Measuring ecosystem goods and services. Ottawa, Canada. 38

39 Acknowledgements Materials prepared by: Michael Bordt Regional Adviser on Environment Statistics ESCAP Statistics Division Materials adapted from: UNSD (Environment Statistics) FDES presentations: Tanzania: Togo: ml (aussi en Français) 39