Integrated assessment

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Integrated Assessment ( IA for short , Integrated Assessment or Integrated Impact Assessment ) describes procedures, especially in the environmental sciences , which combine and examine interdisciplinary knowledge from various specialist areas and evaluate and present the results with regard to alternative courses of action. IA aims to capture cause and effect chains as completely as possible for complex problems. Mostly Integrated Assessment Models ( IAM , dt. Integrated Assessment Models ) are used, which try to integrate the models of the different disciplines into a consistent overall model. Integrated assessment is not a clearly defined term; many earlier research approaches can also be subsumed.

The approach has become particularly important in the analysis of the consequences of global warming and climate policy . The application of IA to other problems in environmental sciences is often referred to as Integrated Environmental Assessment ( IEA ), and the models accordingly as Integrated Environmental Assessment Model ( IEAM ).

Goals and Methods

Integrated assessment is often described in a very general way as an iterative process that starts with a specific political question and in the course of which knowledge and models across various natural and social science disciplines are made understandable, integrated and the results are processed for political decisions. The aim is to create added value compared to approaches from just one discipline and to provide decision-makers with additional information. On the other hand, the focus is not on gaining new knowledge in individual disciplines or developing general models that go beyond the specific issues of the assessment.

IA mostly wants to integrate causal chains of effects including important feedback , for example schematically:

socio-economic drives → economic activities → pressure on the environment, for example in the form of emissions → physical consequences for ecosystems and societies → socio-economic consequences

Other dimensions of integration are those of the stakeholders involved in the process and those of different sizes. Problems examined by means of IA are often long-term and extensive, and occasionally also regional and medium-term.

Integrated models serve to investigate the consequences of political trade or to determine optimal action and to improve the understanding between the actors during the process. Due to their complexity, IAMs are usually implemented as simulation models . In addition, in areas inaccessible in modeling, participatory methods are used, such as focus groups or expert panels. IAMs are often structured modularly and contain, often simplified, partial models from various disciplines.

Uncertainty is a major problem in IAM. A higher level of detail can partially remedy this, but leads to longer running times for simulations and, due to additional parameters, to more complex sensitivity analysis . Often attempts are made to systematically determine the possible range of results, but this is only possible in models that are not very detailed. Therefore, and also due to the different backgrounds and perspectives of the modelers, different models often differ greatly in terms of which aspects they take into account formally and in what level of detail.

A key differentiator of IAM is the way in which policies are integrated. For example, for IAM of global warming:

  • using externally specified scenarios, for example emission scenarios,
  • by specifying technology paths and their emission intensity,
  • or as a result of the activity of agents in the model.

development

The emergence of the term in the 1970s is closely linked to the new technical possibilities of the time, especially computer simulation. The term was probably first used when studying acid rain . The IA and the RAINS ( Regional Acidification Information and Simulation ) model it developed played an important role in the creation of the Geneva Air Quality Control Agreement ; many countries followed the IA's recommendations for the additional protocol on sulfur emissions.

IA gained particular importance in the mid-1980s for investigating the consequences and possible courses of action for anthropogenic global warming. IAMs were used even before the Intergovernmental Panel on Climate Change (IPCC) was founded in 1988. During the negotiations on the United Nations Framework Convention on Climate Change (adopted in 1992), the IAM's results were primarily used to identify “safe emission corridors” that should avoid both a dangerous rise in temperature and unacceptable economic disruptions. The emissions reduction targets of the Kyoto Protocol were also set on this basis . By the mid-1990s there were already more than 50 IAMs. Since then there have been increased efforts to incorporate participatory approaches into the IA of climate policy, for example in the EU's ULYSSES project.

Towards the end of the 1990s and the first half of the 2000s, initiatives such as the European Forum on Integrated Environmental Assessment (EFIEA) or the Integrated Assessment Society ( TIAS ) and specialist journals were launched. However, some have since been reinstated.

In addition to the problem of global warming, IA is now used in a number of other questions, such as the management of land and water use or the environmental impact of chemicals.

Integrated Global Warming Assessment

IA global warming has three main goals:

  • examine possible future paths of human and natural systems in a coordinated manner,
  • To gain insights into key issues of political design options,
  • Prioritize research areas to better find robust policy options.

The integration helps to coordinate assumptions from different disciplines and to introduce feedback into the analysis that would be missing in the isolated investigation of individual fields.

IAMs usually contain at least one climate model and one economic model, such as a general equilibrium model, as partial models . They combine the emission of greenhouse gases caused by human activity, their concentration in the atmosphere, the associated temperature changes, the consequences of global warming for ecosystems and people and their social and economic repercussions. Many IAMs are optimization models that try to determine the emission path with maximum overall benefit.

The second progress report of the IPCC distinguishes between optimizing and evaluating IAMs of climate policy.

The optimizing model types include:

Cost-benefit models
Cost-benefit models try to balance the marginal costs of avoidance measures with those of adaptation measures and thus to determine the optimal combination of emission reduction and adaptation. Emission values ​​are not specified here, but rather the result of the model analysis. Such models must quantify monetary and, insofar as they take them into account, non-monetary damage in uniform quantities.
Goal-based models
Target-based models are based on given emission targets or consequences, such as the 2-degree target , and try to find optimal emission paths or possible courses of action that achieve these targets (see price-standard approach ).
Uncertainty Based Models
Uncertainty-based models deal primarily with decision-making under uncertainty . They include uncertainty in simplified cost-benefit or goal-based models, for example as a spectrum of possible parameter values, or supplement states in complete cost-benefit models. Many also allow policy to be changed in the course of the simulation if uncertainties decrease over time.

Evaluating model types often contain more or more detailed scientific components, while socio-economic components are less pronounced. For example, they also include changes in land use or sulfur emissions. They include:

Deterministic projection models
In deterministic projection models, parameters are given a single, unambiguous value and deterministic output values ​​are determined which are intended to clearly describe long-term future developments.
Stochastic projection models
In stochastic projection models, input or output values ​​are treated using stochastics .

By the mid-1990s, there were already more than 50 IAMs studying global warming. One of the first was IMAGE-1 ( Integrated Model to Assess the Greenhouse Effect ) from the Dutch State Institute for Public Health and Environment ( RIVM ). Further examples are the RICE and DICE ( William D. Nordhaus ) or WIAGEM ( Claudia Kemfert ).

Many frequently cited cost-benefit models, for example DICE , come to the conclusion that the optimal climate policy consists in doing relatively little at first and only acting more clearly later. Some economists attribute this to questionable assumptions, for example a relatively high discounting of future damage, a questionable assessment of non-monetary damage (such as the value of human life or biodiversity ), the neglect of uncertainties or the overestimation of avoidance and underestimation of adaptation costs. In current IAMs, such as those used in the USA for legal regulations, the damage functions are outdated and are primarily based on specialist literature from the 1990s.

From a global warming of more than 3 ° C, many economists estimate that IAM can no longer deliver reliable extrapolations. They think it makes more sense in the IA to view climate policy as an insurance against the worst possible but unlikely event of a climate catastrophe and, accordingly, to prefer target- and uncertainty-based models.

More recent developments also try to model uncertainties and risks. In 2017, William Nordhaus updated his DICE model and also took into account uncertainty in some parameters. Compared to 2013, the tax that would have to be levied on the emission of one ton of CO 2 in the case of an economically optimal climate policy has risen by 50% in his model . Uncertainty leads to an increase of around 15%. Another model, which takes into account the risk given by tipping elements in the earth system and their interactions, suggests that a climate policy that adheres to the 1.5 degree target would be optimal.

criticism

Even if the basic benefits of Integrated Assessment Modeling are not disputed, the approach is repeatedly criticized for having disadvantages that are difficult to remedy. The values ​​of certain parameters for modeling the complex relationships are often set "arbitrarily", which leads to "great effects in the resulting results". The discussion of extreme events (tail risks) is also difficult to take into account, which often necessitates the involvement of separate experts.

See also

literature

Books:

  • Mark E. Jensen, Patrick S. Bourgeron (Eds.): A Guidebook for Integrated Ecological Assessments . 2001, ISBN 0-387-98583-2 .

Trade journals:

Web links

Individual evidence

  1. ↑ a b c d e J. P. van der Sluijs: Integrated Assessment, Definition of . In: Encyclopedia of Global Environmental Change . 2002, ISBN 0-471-97796-9 , pp. 249-253 .
  2. ↑ Parker et al .: Progress in integrated assessment and modeling . In: Environmental Modeling & Software . tape 17 , 2002, p. 209-217 .
  3. ↑ a b c Ferenc L. Toth and Eva Hizsnyik: Integrated environmental assessment methods: Evolution and applications . In: Environmental Modeling and Assessment . tape 3 , 1998, p. 193-207 .
  4. ^ A b c R. SJ Tol and P. Vellinga: The European Forum on Integrated Environmental Assessment . In: Environmental Modeling and Assessment . tape 3 , 1998, p. 181-191 .
  5. ^ A b c Edward A. Parson and Karen Fisher-Vanden: Thematic Guide to Integrated Assessment Modeling of Climate Change . Ed .: Center for International Earth Science Information Network [CIESIN]. 1995 ( sedac.ciesin.columbia.edu ).
  6. ↑ a b Weyant et al .: Integrated Assessment of Climate Change: An Overview and Comparison of Approaches and Results . In: Climate Change 1995, Economic and Social Dimensions of Climate Change, Contribution to Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change .
  7. ^ LH Goulder and WA Pizer: Climate Change, Economics of . In: SN Durlauf and LE Blume (Eds.): The New Palgrave Dictionary of Economics . 2008, doi : 10.1057 / 9780230226203.0247 .
  8. ^ A b Frank Ackerman et al .: Limitations of integrated assessment models of climate change . In: Climatic Change . 2009, p. 297-315 , doi : 10.1007 / s10584-009-9570-x .
  9. ↑ Maximilian Auffhammer: Quantifying Economic Damages from Climate Change . In: Journal of Economic Perspectives . No. 4 , 2018, doi : 10.1257 / jep.32.4.33 .
  10. ↑ Nicholas Stern: The Structure of Economic Modeling of the Potential Impacts of Climate Change: Grafting Gross Underestimation of Risk onto Already Narrow Science Models . In: Journal of Economic Literature . tape 51 , no. 3 , 2013, p. 847-849 , doi : 10.1257 / jel.51.3.838 .
  11. ^ William Nordhaus: Projections and Uncertainties About Climate Change in an Era of Minimal Climate Policies (=  NBER Working Papers . No. 22933 ). September 2017 ( nber.org ).
  12. ↑ Yongyang Cai, Timothy M. Lenton and Thomas S. Lontzek: Risk of multiple interacting tipping points should encourage rapid CO 2 emission reduction . In: Nature . March 2016, doi : 10.1038 / nclimate2964 .
  13. ↑ Robert S. Pindyck: The Use and Misuse of Models for Climate Policy . In: Review of Environmental Economics and Policy . tape 11 , no. 1 . Oxford University Press, S. 100-114 , doi : 10.1093 / reep / rew012 .