Framework for the Development of Environment Statistics (FDES 2013) 10 Administrative records 1.26. Administrative data kept by government agencies or NGOs may be used for the produc tion of environment statistics. Government agencies keep administrative records of the popu lation, households and establishments in response to legislation or regulations, or for internal management purposes. While most administrative data have been obtained traditionally from government agencies, administrative records kept by NGOs (e.g., industry or services associa tions and environmental associations and groups) may also be of use for environment statistics. 1.27. The main advantage of administrative data sources is that it is usually much less costly to collect such data than to create and conduct a survey. The level of response burden is mini mized and complete coverage of units under administration is assured. However, there are usually differences between administrative and statistical terms and definitions; deliberate misreporting may occur; data may not be checked or validated for statistical purposes; restric tions may be placed on access to data; and coverage, though complete for administrative pur poses, might not match statistical requirements. Remote sensing and thematic mapping 1.28. Remote sensing is the science of obtaining information about objects or areas from a distance, typically from aircraft or satellites. Sensors are able to detect and classify objects on, above or below the earth’s surface. Remote sensing makes it possible to collect data on dangerous or inaccessible areas or to replace costly and slow data collection on the ground, thus ensuring that areas or objects are not disturbed. Using satellite, aircraft, spacecraft, buoy, ship, balloon and helicopter images, data are created to analyse and compare, for example, the impact of natural disasters, changes in the area of soil erosion, the extent of pollution, changes in land cover or population estimates of animal species. These can be mapped, imaged, tracked and observed. Combined with thematic mapping data and sufficient validation using actual measurements in the field, remote sensing usually provides consistent, high-quality data for environment statistics. 1.29. Environmental geographic data are geographically referenced (georeferenced) infor mation that includes digital maps, satellite and aerial imagery, other data sources that are linked to a location, coordinate or a map feature, and is all structured in databases. These data provide much of the visualization and contextual elements that add significantly to the quantity and quality of information organized within the framework of environment statistics, particularly when stored in geographic information systems (GIS). GIS is an integrating tech nology that helps to capture, manage, analyse, visualize and model a wide range of data with a spatial or locational component. Such systems allow environmental conditions to be mapped, measured and modelled. Monitoring systems 1.30. Monitoring systems for the production of environment statistics typically comprise field-monitoring stations, which are used to describe the qualitative and quantitative aspects of the environmental media (e.g., air, water or soil quality, or hydrological or meteorological characteristics). The main advantages of these data are that they (i) are usually collected using verifiable scientific methods, (ii) are usually validated, (iii) are often available as time series; and (iv) frequently use models to improve data quality. 1.31. The disadvantages of data from monitoring systems result from the fact that field moni toring stations, especially those monitoring concentrations of pollutants in the environmental media, are usually located in “hot spot” areas with high levels of pollution, high sensitivity or
11
Overview of Environment Statistics—Characteristics and Challenges
11
large numbers of the population being affected. Therefore, the measurements will be location-
specific and more difficult to aggregate over space to produce measures of quality over larger
territories.
Scientific research and special projects
1.32.
Scientific research programmes focus on specific scientific areas. The data collected
and produced will thus depend on the focus of the research. Many such special projects may
be relevant to environment statistics, such as studies on glacier retraction and global CO2
concentration, and biological assays to measure environmental pollutants. Special projects
undertaken to address domestic or international demand often produce research data that
are collected by universities, as well as other research agencies and organizations that may be
governmental or non-governmental. Their main purposes are usually to fill knowledge gaps,
assess the effectiveness of different measures and develop alternative policies.
1.33.
The main advantages of using data from scientific research and special projects are that
they (i) are usually available at no or low cost, (ii) minimize the response burden, (iii) can be
used to address data gaps and (iv) are useful for developing coefficients for models. Disadvan
tages of using these sources include that (i) they often use terms and definitions that differ from
those used in statistics, (ii) access to microdata may be limited, (iii) metadata may be missing,
(iv) data are often available only for case examples (i.e., limited areas or industries) and (v) data
are often available on a one-time basis only.
1.34.
Process-specific technological parameters of production and consumption processes
relating to the input of natural resources and the output of residuals constitute a special cat
egory of data used in environment statistics. These data are used to produce per unit factors or
coefficients that support the calculation and estimation of the resource and emission intensity
of production and consumption processes.
1.35.
Table 1.1 shows the main types of sources from which environment statistics are usually
derived.8 It provides examples of these statistics, the general advantages and disadvantages of
each type of source and the challenges that these sources pose for developing countries.
8 United Nations Economic
Commission for Latin America
and the Caribbean (2009).
Methodological Guide for
Developing Environmental
and Sustainable Development
Indicators in Latin American
and Caribbean Countries.
Manuales series No. 61, available
from www.cepal.org
/es/publicaciones/5502-guia
-metodologica-desarrollar
-indicadores-ambientales
-desarrollo-sostenible (accessed
4 August 2017).
Framework for the Development of Environment Statistics (FDES 2013) 12 Table 1.1 Types of sources of environment statistics and their main characteristics Type of source Examples of source Examples of statistics Examples of advantages Examples of disadvantages Challenges for developing countries Statistical surveys (i) Censuses Censuses such as population and housing, economic, agricultural or other sectoral censuses may include environmental aspects. Specific environmental censuses may cover establishments engaged in activities such as water management or waste management. •• Drinking water supply •• Basic sanitation •• Waste management •• Housing quality •• Use of fertilizers and pesticides in agriculture More representative of the universe of inform ants, more accurate data outcomes •• Low periodicity •• Expensive Requires that sections of the instru ment be refined to capture more and better environmental information (ii) Sample surveys Includes general purpose instruments (which may cover environmental issues) such as household surveys, business surveys and other sectoral surveys. Also includes emerg ing surveys specifically designed to gather environmental information, i.e., environ mental management surveys for business establishments (including industry, tourism and agriculture), municipal environmental management surveys and public opinion polls on the environment, among others. •• Drinking water •• Basic sanitation •• Housing quality •• Establishments with environ mental management systems •• Production and handling of solid waste •• Opinion barometers on environmental policies and management Greater periodicity and therefore more frequently updating of data series Sampling and representativeness of sample may be a concern in the case of surveys designed for other than environmental purposes •• Requires that sections of recurring instruments be refined to capture more and better environmental information •• Requires developing and maintain ing specialized environmental surveys of different sectors and on different levels Administrative records Use, for statistical purposes, of records maintained by different government and non-governmental agencies for administrative purposes, at various levels (including national, regional, provincial and municipal) such as: customs records (imports); sectoral ministry records; public finance and budget records; tax returns records; and environmental authority records. •• Number of motor vehicles •• Environmental licensing •• Designation of protected area •• Environmental education actions •• Public spending on environ mental protection High production periodic ity (annual, quarterly and even monthly) and thus high updating frequency Terms and definitions may differ from those used in statistics; access to microdata may be limited; metadata may be missing •• Requires building statistical capacities in sectoral ministries and public services •• Requires stable national inter-insti tutional coordination Remote sensing and thematic mapping All kinds of remote sensing and atmospheric measuring tools that produce images and their interpretation: satellite imaging; aerial photography; geodata; geodesy; and geomatics. •• Satellite imaging to inventory forests •• Remote imaging of urban sprawl (city surface) •• Land cover and land use (types) •• Level, height or retraction of principal glaciers •• Very accurate •• Costs of imaging have fallen sharply •• High cost of interpreting images •• Few national statistical offices and Ministries of the Environment have geomatics specialists •• Requires geospatial literacy among officials responsible for environment statistics •• Requires sufficient resources to inter pret images and build geospatial representations of data Monitoring systems Includes various quality and pollution moni toring stations and networks such as: urban air pollution monitoring stations; sur face water quality monitoring systems; glacier monitoring systems; and seawater or coastal water quality monitoring systems. Meteorological, hydrological monitoring networks. Various parameters sampled to establish: •• quality of drinking water; •• urban air quality; •• coastal—marine pollution; and •• temperature, precipitation and water flows of rivers. In general, good to excel lent quality and more accu rate data and microdata •• High cost of installing and main taining monitoring systems and thus of producing microdata •• Point specific measurements usu ally do not allow for aggregation over space unless the network is dense enough Requires coordinating the flow of data from primary source in terms of periodicity, aggregation and format required for input into statistical production (series, indicators) Scientific research and special projects Data collected by universities, research agen cies and organizations to fill knowledge gaps and assess effectiveness of or develop alterna tive policies •• Ecosystem health •• Diversity and population trends of selected species •• Characteristics of solid waste •• Process specific technological parameters of residuals •• Low cost •• Minimize response burden •• May be used to fill in data gaps •• Useful for developing coefficients •• Terms and definitions may differ from those used in statistics •• Access to microdata may be limited •• Metadata may be missing •• Often have limited scope and often produced on a one-time basis Requires close collaboration between statisticians and experts from the vari ous scientific fields
13
Overview of Environment Statistics—Characteristics and Challenges
13
1.6. Classifications and other groupings relevant to
environment statistics
1.36.
Statistical classifications are sets of discrete categories which may be assigned to specific
variables registered in a statistical survey or an administrative file and used to produce and
present statistics.9
1.37.
The field of environment statistics has no single overarching internationally agreed
classification of the environment for statistical purposes, such as the International Standard
Industrial Classification of All Economic Activities (ISIC).10 Instead, there are many coexist
ing and emerging classifications and categorizations for specific subject areas. These include
standardized statistical classifications, as well as less formalized groupings or categories. Some
of the classifications and categories that have been used in the environmental field have not
been developed specifically for statistical purposes and therefore must be linked to statistical
classifications.
1.38.
Standard economic and social-demographic statistical classifications, such as ISIC
and the Central Product Classification (CPC),11 or the International Classification of Diseases
(ICD),12 among others, are relevant for and used in environment statistics. The use of these
classifications facilitates the integration of environment statistics with economic and social-
demographic statistics.
1.39.
The pioneering environment statistics classifications adopted by the Conference of
European Statisticians (CES) have been used extensively for international data collection. These
classifications, developed by the United Nations Economic Commission for Europe (UNECE),
are heterogeneous, and most include more than one single hierarchical classification. They also
include recommendations for definitions, measurement methods and tabulations. The UNECE
Standard Statistical Classifications for the environment include classifications of Water Use
(1989), Land Use (1989), Wastes (1989), Ambient Air Quality (1990), Surface Freshwater Quality
for the Maintenance of Aquatic Life (1992), Marine Water Quality (1992), Environment Protec
tion Activities and Facilities (1994), and Flora, Fauna and Biotopes (1996). These classifications
have been used extensively by the UNECE, the Organisation for Economic Co-operation and
Development (OECD), Eurostat, UNSD and various regional and national bodies for interna
tional data collection.
1.40.
More recent statistical classifications, as well as less-formalized categorizations which
pertain to specific subdomains of environment statistics, have been developed by interna
tional organizations, specialized agencies, intergovernmental organizations or NGOs. Exam
ples include the Food and Agriculture Organization of the United Nations (FAO) Land Cover
Classification System (LCCS) and the groupings and classifications developed for water statis
tics and energy products included in the International Recommendations for Water Statistics
(IRWS)13 and the International Recommendations for Energy Statistics (IRES).14
1.41.
Many of the aforementioned classifications have been revised, adapted and used in the
SEEA Central Framework (SEEA-CF), including the Classification of Environmental Activi
ties (CEA), which covers the classes of activities considered to be environmental protection
and resource management activities, used primarily to produce statistics on environmental
protection and resource management expenditure. Other examples include the categories of
solid waste or the interim classifications of land use and land cover. Additional work on clas
sifications of ecosystem services is being conducted as part of the development of the SEEA
Experimental Ecosystem Accounting.
1.42.
There are also classifications and lists of categories that do not originate in the statistical
community but are used in environment statistics, such as the classifications of natural and
technological disasters produced by the Centre for Research on the Epidemiology of Disasters
9 United Nations Statistics Division
(1999). Standard Statistical
Classifications: Basic Principles,
available from https://unstats
.un.org/unsd/class/family
/basicprinciples_1999.pdf
(accessed 4 August 2017).
10 United Nations Statistics Division
(2008). International Standard
Industrial Classification of All
Economic Activities (ISIC), Rev. 4,
available from http://unstats
.un.org/unsd/cr/registry/isic-4
.asp (accessed 4 August 2017).
11 United Nations Statistics
Division (2008). “Central Product
Classification, Ver. 2”, available
from http://unstats.un.org/unsd
/cr/registry/cpc-2.asp (accessed
4 August 2017).
12 World Health Organization
(2011). “International
Classification of Diseases”,
available from www
.who.int/classifications/icd/en/
(accessed 4 August 2017).
13 United Nations Statistics
Division (2012). International
Recommendations for Water
Statistics, available from
http://unstats.un.org
/unsd/envaccounting/irws
/irwswebversion.pdf (accessed
4 August 2017).
14 United Nations Statistics
Division (2011). International
Recommendations for Energy
Statistics (draft version), available
from https://unstats.un.org
/unsd/energy/ires/IRES_edited2
.pdf (accessed 4 August 2017).