Neo-liberal State
3.5 Sustainable Development
3.5.2 Conceptual Approaches from Different Angles
Over this long period, very different approaches have been developed (partly in parallel streams and communities) to bring the interactions between humans and the environment into a logical framework in order to create order and structure in the ocean of possible and actual information. In the meantime, many of these approaches have been incorporated into the System of Environmental-Economic Accounting SEEA (United Nations2014b) (a UN statistical standard) and its exper- imental part, which addresses open methodological issues of ecosystem accounting
(United Nations2014c). In contrast to the System of National Accounts, the SEEA is composed of several methodological approaches under one roof80:
• Flow accounts, which ultimately can be traced back to the approaches of inter- preting the economy as an organism whose metabolism can be represented in a meaningful way by input–output methods (Ayres and Simonis1994; Radermacher and Stahmer1998).
• ‘Footprints’, which pursue the idea of a cumulation of all environmental pressures (be it the emission of greenhouse gases or the use of land) that arise ‘from cradle to grave’ for a good, i.e. over the entire production route, including all energy inputs and transport routes, wherever these take place, at home as well as abroad.81 Input–output analysis methods offer the appropriate tools for this, provided the corresponding input–output tables (e.g. World IoTs) are available. In a globalised world, such statistics are particularly important for quantifying the cross-border effects of production and consumption, i.e. the export and import of environmental pollution.
• Inclusion of the nature (and its services) in the capital accounts to determine whether or not a nation’s capital has been kept intact over a period (Radermacher and Steurer2015; De Smedt et al.2018). In this way, the economic method of accounting for long-term effects resulting from the use and depreciation of fixed assets is also applied to nature. The future with the interests of the then living generations thus becomes part of today’s balance sheet.
• A systematic and consistent presentation of economic activities, such as goods, services and taxes that are directly related to environmental issues. In contrast to the previous modules, the system boundaries of the economy are not extended here. Rather, it is a matter of subdividing existing aggregates with finer granulation in order to be able to analyse environmentally relevant changes over time.
• A monetary valuation of natural goods and their services through methods applied in environmental economics is handled cautiously in SEEA for many reasons.
Although this would allow a higher degree of compression and aggregation of information to a few indicators, the inevitable value judgement load is considered to be incompatible with the quality profile of official statistics (Radermacher and Steurer2015; Cook2017).
Parallel to the accounting methods described above, basic statistics in the field of the environment have also developed considerably. The corresponding international guideline is the Framework for the Development of Environment Statistics (FDES) of the United Nations (United Nations2017a). The distinction between Driving forces, environmental Pressures, the State of the environment and Responses of society (DPSR) has played a crucial role in the systematics of environmental statistics as well as in indicators in the environmental field. For a further statistical decomposition
80See, for example, https://ec.europa.eu/eurostat/statistics-explained/index.php/Environmental_
accounts_-_establishing_the_links_between_the_environment_and_the_economy#Introduction_
to_environmental_accounting.
81See, for example, the calculation of Total Material Requirementhttps://www.eea.europa.eu/
publications/signals-2000/page017.htmlt.
of the different driving forces (population, affluence and technology), the I(mpact on the environment)=P(opulation)×A(ffluence)×T(echnology) ‘formula’ (Ehrlich and Holdren John 1971) can be used additionally in order to integrate statistical information other than environmental into the picture. Like all alternative approaches mentioned, this ‘formula’ must not be overinterpreted as a mathematical or physical equation. Rather, it is suited to draw attention to the relationships, modes of interac- tion and feedback between demographic development, prosperity and technological progress. In this way, it is possible, for example, to place statistical information on demographic developments (global and national) in the context of improvements in living conditions (income, health, education, etc.) (Rosling et al.2018; Population Matters2018) and to derive the resulting additional environmental burdens from the use of the existing technologies (production methods, mobility, food, etc.).
Geographic information systems (GIS), geocoded data and geostatistical methods have developed continuously since the late 1980s and offer correspondingly long time series, for example on land cover and land use and their changes over time.82What is decisive here is the opening-up of completely new possibilities for linking basic data via their spatial relationship. When considering the accounting of ecosystems and biodiversity because their services are vital for the survival of mankind, it is necessary firstly to record these systems in a statistically representative way (analogous to, for example, population statistics) and secondly to use appropriate statistical methods to aggregate such large amounts of data into meaningful indicators.83With the current availability of large sets of data from remote sensing, the first condition will be easier to fulfil than in the past. Nevertheless, remote sensing data must be combined with (costly and cumbersome) results from field surveys in smart GISs if progress is to be made in this area (Radermacher et al.1998).
The disadvantage of many of the methodological approaches described so far is that they do not adequately reflect the complexity of the interdependencies between economic, social and ecological systems, i.e. they are too reductionist. Therefore, a complementary approach is increasingly being pursued, which deals with these systems, their vulnerability/robustness, any existing tipping points and the risks of crossing such sensitive boundaries. “In a systems approach, the focus moves from measuring the stocks of assets to coming to grips with the resilience of economic, societal and natural systems. Tackling these issues requires interdisciplinary work, with a focus on the ability of the system to cope with risks and uncertainties in a broad and long-run perspective, and on the different ways to manage this coping
82See, for example, the archive of European data from CORINE Land Cover https://
land.copernicus.eu/pan-european/corine-land-cover or of LUCAS https://esdac.jrc.ec.europa.eu/
projects/lucas and https://ec.europa.eu/eurostat/statistics-explained/index.php/LUCAS_-_Land_
use_and_land_cover_survey.
83In Europe, corresponding activities are coordinated within ‘the Mapping and Assessment of Ecosystems and their Services (MAES)’ https://biodiversity.europa.eu/maes, supported by the knowledge innovation project on an ‘Integrated System of Natural Capital and Ecosystem Services Accounting in the EU (INCA)’http://publications.jrc.ec.europa.eu/repository/handle/JRC110321.
At UN level, experimental ecosystem accounting has made progress as well (https://seea.un.org/
events/forum-experts-seea-experimental-ecosystem-accounting).
Fig. 3.12 Relationship of basic statistics, accounting systems and indicator sets. From Framework for the Development of Environment Statistics (FDES 2013), by UN Department of Economic and Social Affairs—Statistics Division, ©2017 United Nations. Reprinted with the permission of the United Nations (2017a: 25)
ability (resilience) of systems. “Resilience” is indeed referred to in the Sustainable Development Goals (SDGs) and by the targets of the 2030 Agenda” (De Smedt et al.
2018).
Indicator approaches differ fundamentally from the methods mentioned before (see Fig. 3.12). The main purpose of indicators is usually to define quantitative measures for a particular policy direction or programme in such a way that these
‘metrics’ can also be used to set quantitative targets and to monitor the achievement of objectives. While basic statistics and accounts are multipurpose, indicators are closely linked with a specific application. This makes a huge difference in the way indicators have to be designed, produced and communicated. Indicators are not a simple subcategory of statistics. With their earmarking, completely different driving forces and intended functionalities come into play, which must be taken into account.
Above all, however, it is important to understand indicators as one product in the portfolio of official statistics. Indicators must be closely linked to, build on and lead to the other products for further analysis (see Fig.3.13).
The 2014 Recommendations of the Conference of European Statistics give a comparatively complete overview of the progress made in the areas of human well- being ‘here and now’, ‘later’ and ‘elsewhere’. Noteworthy is the assessment in the supposedly simplest of these three dimensions, the ‘here and now’: “There is no theo- retical consensus on how to measure the human well-being of the present generation”
(UNECE2014, p. xviii).
In the theoretical and statistical analysis of interactions between the natural, the social and the economic systems in the sense of nonlinear and erratic changes, resilience or vulnerability, planetary boundaries, etc., the development of corre- sponding metrics is comparatively at the beginning84and a comprehensive standard
84The Report of the High-Level Expert Group on the Measurement of Economic Performance and Social Progress (HLEG) was released during the 6th OECD World Forum on Statistics, Knowledge and Policy on 27–29 November 2018 in Incheon, Korea; this aspect is covered in the reporthttps://
www.oecd.org/statistics/measuring-economic-social-progress/(Stiglitz et al.2018b).
Fig.3.13Narratives,heuristicsandframeworksusedinstatisticsoftheenvironment
of statistical measurement (if at all possible) for these domains is still a long way off, even if presumably comprehensive indicator lists are floating around (O’Neill et al.
2018).