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Development of environmental indicators in UNEP

M. Schomaker, Division of Environment Information and Assessment,
United Nations Environment Programme (UNEP), Nairobi, Kenya

This presentation will go slightly beyond the issue of indicators and consider the entire array of information and data collection, management, and analysis, since they are all relevant to indicators for assessing and reporting on sustainable development. First the United Nations Environment Programme (UNEP) setting will be outlined, then a number of indicator-related issues will be touched upon as part of assessment and reporting, finishing with a listing of specific UNEP work on land quality indicators.


It should be realized that UNEP, unlike FAO, is not an implementing agency. Its main role is in catalyzing actions, liaison and conflict resolution. UNEP tries to promote activities by providing small financial contributions resulting in joint outputs, by jointly developing frameworks others can link up to and by providing expert advice. All its activities take place as joint efforts with other institutions (both within and outside the UN; at international, regional and national level). UNEP's mission is broad, working mostly at global and regional levels and covering "the environment" as a whole:

"To provide leadership and encourage partnership in caring for the environment by inspiring, informing and enabling nations and people to improve their quality of life without compromising that of future generations."

Within this general context UNEP's assessment mandate is:

"To keep under review the state of the world environment (SOE); enhance understanding of the critical linkages between environment and human activities; identify priorities for international action; flag emerging issues; and strengthen national, regional, and global information handling capacities."

UNEP's assessment framework for fulfilling this mandate can be illustrated in Figure 1, in which all groups of activities needed for integrated assessment and reporting are reflected. Often there are not enough reliable, well structured, easily accessible data available; often progress made in a project or programme is based on expert opinion and educated guesswork. Besides, there are no straightforward, internationally-agreed methodologies for integration of natural resources data with socio-economic data; for scale integration (both spatial and temporal); and for change indicators. Assessment efforts still tend to take place on an isolated case study or subject level. There are, among others, different schools of thought, different methodologies, different entry points, data availability and compatibility problems, scale problems and different ways of presenting information.

FIGURE 1. Providing the knowledge base for decision making

In the current situation the assessment triangle looks more like a pinnacle and perhaps even an up-side-down one: information used to take decisions often originates from weak data and analysis. Eventually we would want the pinnacle to look like a more stable triangle (Figure 2).

FIGURE 2. From pinnacle to triangle


To move from pinnacle to triangle we need to: (i) continue efforts to further develop operational, practical data collection and data management tools; analysis, integration and modelling methodologies; indicators and presentation formats; and (ii) continue efforts to enhance capacities in the entire assessment process. An enormous challenge lies ahead.

Issues, often interrelated, involved in data and information management for and assessment of for instance land quality can be summarized as follows:

1. user relevance;
2. integration;
3. scale issues;
4. methodological and science issues;
5. data issues;
6. assessment capacity.

User relevance

Many assessment activities still take place within the realm of science. More direct links are necessary between the actual users and producers of information. Assessment activities should preferably be formulated together with users, in fact upon the request of users (for their purposes). Depending on who wants to know, different levels of detail and different forms of information are needed. Once the "why" is clear the kind of data needed can be decided upon. Users are often considered in the context of the decision-making cycle which includes four stages (Figure 3).

FIGURE 3. The decision-making cycle (adapted from Winsemius 1986, in RIVM/UNEP, 1995)

Decision-making processes take place at all levels of government and involve many different cultural, social, institutional, economic and environmental inputs and considerations. Depending on what level and which stage in the cycle, the kind of information needed differs.

For problem identification and awareness raising, general descriptive indicators are needed. But even then, different audiences need customized material; for instance to reach national policy-makers one would opt for a different presentation than for the general public.

For strategy, policy, project formulation one would need more detailed indicators, also focusing on the causes of a certain problem and on projections of impacts, through modelling, scenarios, cost-benefit and multi-criteria analysis, so that effective, and realistic, responses can be formulated.

For the actual implementation of land quality related policies, goals and targets need to be established at national and local level (more quantitative indicators). Here the social and economic context becomes increasingly important. The people living on the land will have to decide on what is needed and on how and when they want to and can reach certain targets. Assessment and information aspects should focus on negotiation to agreement on targets among all the stakeholders who in various ways have interests in the land.

To evaluate the effectiveness of policies and actions, one needs to find quantitative indicators that illustrate how the situation has changed in relation to the goals and targets.

In summary: indicators should clearly serve the specific users and stakeholders, both considering the level of aggregation (from local population to high-level international policy-makers) and the stage in the decision-making cycle.


Over the past decades UNEP has been focusing on state and trends, with emphasis on environmental sub-systems such as climate, desertification and biodiversity. However, though it is indeed necessary to know where a problem occurs, one also needs to know why the problem occurs in order to be able to formulate responses. There is an urgent need to focus more on the inter-linkages between the environmental system and the human system (Figure 4) rather than the individual components. Not only should research and assessment activities cover both sub-systems; inter-linkages between the two are even more important.

FIGURE 4. A model of human interaction with the environment (RIVM/UNEP, 1995)

Causes of environmental problems and the resulting negative trend are predominantly human induced. Only when the causes and the impacts of the resulting pressures on the system are known can adequate responses be formulated (Figure 5). To qualify and quantify the pressures, state and responses, indicators need to be found that adequately represent the extremely complex situation. The OECD PSR framework (Figure 5) is being adopted by many for such indicator development (even though it cannot properly reflect the real world because linkages are not linear).

FIGURE 5. Pressure-state-response framework for indicators (RIVM/UNEP, 1995)

There are potentially hundreds of indicators which could be relevant to land quality and desertification (UNEP/RIVM 1994, UNDP/UNSO/NRI 1995, World Bank/FAO/UNDP/-UNEP 1995, UNDPCSD 1995 etc.). Some cover the causes/pressures part of the system, some focus on change in status and trends and the impacts of such change, and some are related to responses. The challenge before us is to find those core indicators that are sufficiently representative and at the same time easy to understand and measure on a routine basis. To put it differently, indicators should be SMART: specific, measurable, achievable, relevant and time-bound. Indicators are needed at different aggregation levels (see also the section on user relevance).

Scale issues

Ideally some persons would want to have detailed data on "everything". We would want to be able to move smoothly from an abundance of detailed field data to summarized information for national level purposes to even more condensed information for sub-regional, regional and eventually global level purposes (the Indicator Pyramid, Figure 6).

FIGURE 6. The Information/Indicator Pyramid (SCOPE, 1995 and WRI, 1995)

For practical reasons (constraints in available time, human and financial resources) we would want to find shortcuts. We would want to determine simple, direct links between field level data, general statistics and remotely sensed data at decreasing levels of detail (through extrapolation, spatial modelling techniques and the like). Once such relationships are established we would be able to monitor over time and to indicate which temporal scales are relevant for which aspects. For many of these wishes there is not yet a response or solution.

Methodological and science issues

This very much relates to the scale issues mentioned above. Most assessment work still takes place on an isolated, scientific case study basis. Methods developed are very site-specific. Work is often carried out within the university realm (PhD studies and the like): an ideal situation where usually more equipment is available than in the real world, where "free" research staff time is at hand, where time pressure may be less acute. As a result, methods developed under these circumstances may not be easily repeatable, not broadly applicable, not realistic in terms of time, cost, and practical applicability within for instance a government or NGO structure, and not suitable to provide an overall picture for larger areas.

Data issues

On the one hand there are not enough data available or being collected on a routine basis; on the other hand, sometimes data are being collected because they have been collected routinely without clear reasons on why they are needed. The quality of data that are available is often questionable: no standardized procedures are followed, guess work is involved and the like. Data are often collected using "self made" definitions and classification systems, as for instance in the case of land use and land cover: data from one area are not compatible with data from other areas, which hampers comparison and presentation of an overall picture. Data may exist but be difficult to get hold of: they are stored in too many different places, are poorly documented and often there is a competition aspect involved. Many data are only available as general statistics and point data while one would ideally want georeferenced information.

Data and information management and assessment capacity

Apart from the need to further develop methodologies for data collection, data management, data analysis and integration, and data presentation, there is a need to strengthen national capacities in all these aspects so that the entire world can eventually contribute to the assessment process on an equal basis.


UNEP work relevant to land quality indicators is listed below, following the five compartments of the Assessment Triangle (Figure 1). Most work is ongoing and is part of UNEP's approved 1996-97 programme. Whether all will be implemented in full will depend on the available funds.

In addition, it should be mentioned that UNEP mainly uses the Environment and Natural Resources Information Networking (ENRIN) programme as the vehicle to strengthen capacities in environmental assessment and reporting and associated data management. This programme develops umbrella frameworks (along the lines of the triangle) for different regions. It encourages major donor institutions in each region to link up and contribute to activities that fit within the umbrella framework. Emphasis is on increasing collaboration among existing institutions, programmes and networks (to avoid duplication). Outputs developed elsewhere are promoted through this programme. These can be any relevant successful output from anywhere: datasets, data management software tools (such as the Soil and Terrain Digital database methodology - SOTER), analysis tools or models and decision-making tools.

Databases and monitoring relevant to assessment of, inter alia, land quality

¤ GEMS/Water and GEMS/Air programmes (air and water quality monitoring networks).

¤ The planned Global Terrestrial Observing System (GTOS), comparable and linked to the already existing Global Oceans and Global Climate Observing Systems (GOOS and GCOS). Once operational GTOS will provide an excellent umbrella mechanism for data collection and sharing; co-sponsors are FAO, International Council of Scientific Unions (ICSU), UNESCO, UNEP and World Meteorological Organization (WMO).

¤ Further methodological development of the GLASOD approach (in Asia); and preparation of (sub-) regional Soil and Terrain Digital databases or shells (SOTER); with ISRIC and FAO.

¤ World Overview of Conservation Approaches and Technologies (WOCAT) and UNEP's drylands management success stories programme; GLASOD showing the negative side (human-induced degradation) and WOCAT, supported by University of Bern, FAO, several bilateral donors, the positive side (successful responses).

¤ Digital Elevation Model (DEM): an elevation database from which many products can be derived, a joint USGS-EROS Data Centre, UNEP, National Aeronautics and Space Administration (NASA) product; available for Africa on the WWW; other continents to follow in 1996.

¤ Land cover characterization using advanced very high resolution radiometer (AVHRR); a joint USGS-EROS Data Centre, UNEP and NASA product, using the International Geosphere-Biosphere Programme (IGBP) processing protocols; Latin America first half 1996; related project implemented for a number of countries in Asia and the Pacific.

¤ Population distribution (through spatial modelling); with CGIAR and NCGIA.

¤ Core Data Working Group activities: focus on core data for integrated environment assessments and for UNEP's new Global Environment Outlook reports (see under Assessment Reports below).

Data and information management (data access; meta-data; data harmonization; GIS; decision support tools; database structures; etc.)

¤ Mercure satellite system which will link UNEP's data and information sources to the Internet and World Wide Web facilities, etc. This will improve data accessibility and sharing. System currently being installed.

¤ Development of meta-data and associated information system for UNEP data and information and for referencing to other data in the world, all in support of assessment and reporting activities. A sub-system for land quality indicators is under consideration (the concepts behind such complicated information systems still need much thought and experimentation).

¤ UNEP/FAO Initiative on Standardizing Land Cover and Land Use Classification Systems, with the Institute of Terrestrial Ecology (ITE), World Conservation Monitoring Centre (WCMC) and International Institute for Aerospace Survey and Earth Sciences (ITC); a flexible attribute-based approach (including software).

¤ International Center for Research in Agroforestry (ICRAF), International Institute for Land Reclamation and Improvement (ILRI) and UNEP work on a tool for spatial characterization (a CDROM with a Data Exploration Tool).

Support of methodology development for assessment

¤ Integration of socio-economic and natural resources aspects and scale integration (case study based, eventually to lead to more generally applicable methodology).

¤ Indicator development (the issue that links all assessment components together): considering both the biophysical and the social dimensions; contribute to ongoing and new initiatives such as Department for Policy Coordination and Sustainable Development (DPCSD), United Nations Sudano-Sahelian Office (UNSO), SCOPE, International Development Research Centre (IDRC), International Institute for Sustainable Development (IISD), World Bank, FAO, UNEP, UNDP Land Quality Indicators programme, UNEP success story analysis and others. There is an urgent need to bring all these efforts on one line.

¤ Forecasting/scenarios/modelling: in the framework of the Global Environment Outlook (GEO) process (see GEO below). For instance some modelling work on linking food production and land degradation, but mainly the even more complicated integrated modelling issues.

Assessment reports

¤ Sectoral assessments, such as: Global Water Assessment; the World Atlas of Desertification.

¤ Integrated outlooks on the global environment - GEOs: biennial and decadal; replacing UNEP's more traditional state-of-the-environment reports; involving much regional consultation; supported by working groups on data, scenarios, modelling, policy; production process still under development; first trial issue early 1997.

¤ Technical reports, datasets, software tools, decision-making tools (e.g., indicators) resulting from the work listed above (for instance a whole range of GEMS/Water and GEMS/Air publications both covering specific assessments and methodological material in the form of guidelines and monitoring standards; publications on social dimension issues; the References list directly indicator related publications).

Information sharing

Outputs will be produced as a family of products. The same base material will be customized for specific target groups: brochures, popular environment library booklets, hands-on booklets, newsletters (e.g., EarthViews, Desertification Bulletin), videos, electronic information sharing, summary reports for policy and decision-makers, more elaborated technical reports and basic data for technicians and the scientific community.

The present paper does not cover indicator work in which UNEP is involved in the context of biodiversity, land-based sources of pollution, oceans and coastal areas, forests, etc.


RIVM/UNEP. 1995. Scanning the global environment: A framework and methodology for UNEP's reporting functions. UNEP Environment Assessment Technical Report 95-01, Nairobi, Kenya.

SCOPE. 1995. Environmental Indicators: Systematic Approach to Measuring and Reporting on the Environment in the Context of Sustainable Development. Paper by the Project on Indicators of Sustainable Development of the Scientific Community on Problems of the Environment (SCOPE) presented at an International Workshop on Indicators of Sustainable Development for Decision-Making, 9-11 January, Ghent, Belgium.

UNDPCSD. 1995. Work Programme on Indicators for Sustainable Development. Presented during the third session of the Commission on Sustainable Development, April 1995.

UNDP/UNSO/NRI. 1995. Development of Desertification Indicators for Field Level Implementation. Report prepared by R. Ridgway of the Natural Research Institute in the UK for UNDP/UNSO.

UNEP/RIVM. 1994. An Overview of Environmental Indicators: State of the art and perspectives. UNEP Environment Assessment Technical Report 94-01, Nairobi, Kenya.

UNEP/UNDPCSD. 1995. The Role of Indicators in Decision-Making. Joint paper by UNEP and the UN Division for Sustainable Development, Department for Policy Coordination and Sustainable Development (DPCSD) presented at an International Workshop on Indicators of Sustainable Development for Decision-Making, 9-11 January, Ghent, Belgium.

UNEP/UNDPCSD. 1996. Report of the Meeting on Integrated Environmental Assessment/Global Environmental Outlook Core Data Working Group, held at the UNDPCSD Offices in New York, 22-23 January 1996. UNEP Environmental Information and Assessment Meeting Report 96-01.

Winsemius. 1986

World Bank. 1995. Monitoring Environmental Progress (MEP): A Report on Work in Progress. March 1995. Draft for discussion purposes only. Environment Department. Washington D.C.

World Bank/FAO/UNDP/UNEP. 1995. Land Quality Indicators. World Bank Discussion Paper 315.

WRI. 1995. Environmental Indicators: A Systematic Approach to Measuring and Reporting on Environmental Policy Performance in the Context of Sustainable Development. World Resources Institute, Washington D.C.

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