What is Policy Analytics? An Exploration of 5 Years of Environmental Management Applications

Abstract

Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of “policy analytics” was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.

This is a preview of subscription content, access via your institution.

References

  1. Arts I, Buijs AE, Verschoor G (2017) Regimes of justification: competing arguments and the construction of legitimacy in Dutch nature conservation practices. J Environ Plan Manag 61(5–6):1070–1084

    Google Scholar 

  2. Azibi R, Vanderpooten D (2002) Construction of rule-based assignment models. Eur J Oper Res 138(2):274–293

    Article  Google Scholar 

  3. Boschet C, Rambonilaza T (2017) Collaborative environmental governance and transaction costs in partnerships: evidence from a social network approach to water management in France. J Environ Plann Man 61:105–123. https://doi.org/10.1080/09640568.2017.1290589

    Article  Google Scholar 

  4. Boyd A, Geerling T, Gregory WJ, Kagan C, Midgley G, Murray P, Walsh MP (2007) Systemic evaluation: a participative, multi-method approach. J Oper Res Soc 58:1306–1320

    Article  Google Scholar 

  5. Cailloux O, Meinard Y (2019) A formal framework for deliberated judgment. Theor Decis. https://doi.org/10.1007/s11238-019-09722-7

  6. Choulak M, Marage D, Gisbert M, Paris M, Meinard Y (2019) A meta-decision-analysis approach to structure operational and legitimate environmental policies—with an application to wetland prioritization. Sci Total Environ 655:384–394. https://doi.org/10.1016/j.scitotenv.2018.11.202

    CAS  Article  Google Scholar 

  7. Christophides V, Efthymiou V, Palpanas T, Papadakis G, Stefanidis K (2019) End-to-end entity resolution for big data: a survey. CoRR.https://arxiv.org/abs/1905.06397

  8. Daniell KA (2012) Co-engineering and participatory water management: organisational challenges for water governance. Cambridge University Press, Cambridge UK.

    Google Scholar 

  9. Daniell KA, Mazri C, Tsoukiàs A (2010) Real world decision-aiding: a case of participatory water management. In: French S, Rios-Insua D (eds) e-Democracy: a group decision and negotiation perspective. Springer-Verlag, Berlin, p 125–150

  10. Daniell KA, Morton A, Ríos, Insua D (2015) Policy analysis and policy analytics. Ann Oper Res 236:1–13. https://doi.org/10.1007/s10479-015-1902-9

    Article  Google Scholar 

  11. Department of Industry, Innovation and Science (DIIS) (2018) Australia’s tech future. Department of Industry, Innovation and Science. Canberra. https://www.industry.gov.au/sites/default/files/2018-12/australias-tech-future.pdf. Accessed 8 Mar 2019

  12. Devictor V, Bensaude-Vincent B (2016) From ecological records to big data: the invention of global biodiversity. HLPS 38:13

    Google Scholar 

  13. Federal Data Strategy (2019) The Federal Data Strategy. https://strategy.data.gov/. Accessed 8 Mar 2019

  14. Federal Ministry for Economic Affairs and Energy (FMEAE) (2018) Digital Strategy 2025. Federal Ministry for Economic Affairs and Energy. https://www.de.digital/DIGITAL/Redaktion/EN/Publikation/digital-strategy-2025.pdf?__blob=publicationFile&v=9. Accessed 6 Mar 2019

  15. Ferretti V, Pluchinotta I, Tsoukiàs A (2018) Supporting decisions in public policy making processes: generation of alternatives and innovation. Eur J Oper Res 273:353–363. https://doi.org/10.1016/j.ejor.2018.07.054

    Article  Google Scholar 

  16. Giordano R, Brugnach M, Pluchinotta I (2017) Ambiguity in problem framing as a barrier to collective actions: some hints from groundwater protection policy in the Apulia Region Group. Decis Negot 26:911–932. https://doi.org/10.1007/s10726-016-9519-1

    Article  Google Scholar 

  17. Giordano R, D’Agostino D, Apollonio C, Scardigno A, Pagano A, Portoghese I, Lamaddalena N, Piccinni AF, Vurro M (2015) Evaluating acceptability of groundwater protection measures under different agricultural policies. Agr Water Manag 147:54–66. https://doi.org/10.1016/j.agwat.2014.07.023

    Article  Google Scholar 

  18. Giordano R, Pluchinotta I, Zikos D, Krueger T, Tsoukiàs A (2020) How to use ambiguity in problem framing for enabling divergent thinking: integrating problem structuring methods and concept-knowledge theory. In: White L, Kunc M, Malpass J, Burger K (eds) Behavioral operational research: a capabilities approach. Palgrave Macmillan Publishers, Basingstoke, UK, p 93–117

  19. Habermas J (1985) The theory of communicative action. Beacon Press, Boston

  20. Habermas J (1990) The philosophical discourse of modernity. MIT Press

  21. Hamilton SH, Fu B, Guillaume JHA, Badham J, Elsawah S, Gober P, Hunt RJ, Iwanaga T, Jakeman AJ, Ames DP, Curtis A, Hill MC, Pierce SA, Zare F (2019) A framework for characterising and evaluating the effectiveness of environmental modelling. Environ Model Softw 118:89–98. https://doi.org/10.1016/j.envsoft.2019.04.008

    Article  Google Scholar 

  22. Howlett M (2011) Designing public policies: principles and instruments. Routledge, London

    Google Scholar 

  23. Jakeman AJ, Letcher RA, Norton JP (2006) Ten iterative steps in development and evaluation of environmental models. Environ Model Softw 21:602–614. https://doi.org/10.1016/j.envsoft.2006.01.004

    Article  Google Scholar 

  24. Jaric I, Quétier F, Meinard Y (2019) Procrustean beds and empty boxes: on the magic of creating environmental data. Biol Conserv 237:248–252

    Article  Google Scholar 

  25. Jeanmougin M, Dehais C, Meinard Y (2017) Mismatch between habitat science and habitat directive: lessons from the French (counter-)example. Conserv Lett 10:634–644

    Article  Google Scholar 

  26. Johnson BD (2011) Science fiction prototyping: designing the future with science fiction. Morgan & Claypool Publishers, San Francisco

    Google Scholar 

  27. Johnston EW (ed) (2015) Governance in the information era: theory and practice of policy informatics. Routledge, New York

  28. Kana V, Somé B, Tsoukiàs A (2014) A new methodology for multidimensional poverty measurement based on the capability approach. Socio-Economic Plan Sci 48:273–289. https://doi.org/10.1016/j.seps.2014.04.002

    Article  Google Scholar 

  29. Kelly RA, Jakeman AJ, Barreteau O, Borsuk ME, ElSawah S, Hamilton SH, Henriksen HJ, Kuikka S, Maier HR, Rizzoli AE, van Delden H, Voinov AA (2013) Selecting among five common modelling approaches for integrated environmental assessment and management. Environ Model Softw 47:159–181

    Article  Google Scholar 

  30. Lahtinen TJ, Guillaume JHA, Hämäläinen RP (2017) Why pay attention to paths in the practice of environmental modelling? Environ Model Softw 92:74–81. https://doi.org/10.1016/j.envsoft.2017.02.019

    Article  Google Scholar 

  31. Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T (2009) Computational social science. Science 323(5915):721–723

    CAS  Article  Google Scholar 

  32. Leroy A, Mousseau V, Pirlot M (2011) Learning the parameters of a multiple criteria sorting method. In: Brafman R, Roberts F, Tsoukias A (eds.). Algorithmic decision theory. Lecture notes in artificial intelligence, vol. 6992. Springer, Berlin, Heidelberg, p 219–233

  33. Löfgren K, Webster CWR (2020) The value of Big Data in government: the case of ‘smart cities’. Big Data Soc 7(1). https://doi.org/10.1177/2053951720912775

  34. Long C (2019) An uncomfortable time to be in politics (or anywhere with a ‘climate’). The New Matilda, December 12. https://newmatilda.com/2019/12/12/an-uncomfortable-time-to-be-in-politics-or-anywhere-with-a-climate/

  35. Maier HR, Guillaume JHA, van Delden H, Riddell GA, Haasnoot M, Kwakkel JH (2016) An uncertain future, deep uncertainty, scenarios, robustness and adaptation: how do they fit together? Environ Model Softw 81:154–164. https://doi.org/10.1016/j.envsoft.2016.03.014

    Article  Google Scholar 

  36. Mazri C, Daniell KA, Tsoukiàs A (2019) Decision support in participative contexts: the organisational design dimension. Int J Decis Support Syst Technol 11:47–80

    Article  Google Scholar 

  37. Meinard Y (2017) What is a legitimate conservation policy? Biol Conserv 2013:115–123

    Article  Google Scholar 

  38. Meinard Y, Tsoukias A (2019) On the rationality of decision aiding processes. Eur J Operational Res 273(3):1074–1084. https://doi.org/10.1016/j.ejor.2018.0

    Article  Google Scholar 

  39. Merritt WS, Fu B, Ticehurst JL, El Sawah S, Vigiak O, Roberts AM, Dyer F, Pollino CA, Guillaume JHA, Croke BFW, Jakeman AJ (2017) Realizing modelling outcomes: a synthesis of success factors and their use in a retrospective analysis of 15 Australian Water Resource Projects. Environ Model Softw 94:63–72. https://doi.org/10.1016/j.envsoft.2017.03.021

    Article  Google Scholar 

  40. Mergel I, Rethemeyer RK, Isett K (2016) Big data in public affairs. Public Admin Rev 76(6):928–937

    Article  Google Scholar 

  41. Midgley G (2006) Systems thinking for evaluation. In: Williams B, Imam I (eds) Systems concepts in evaluation: an expert anthology. Edge Press, Point Reyes, p 11–34

  42. Nabatchi T (2012) Putting the “public” back in public values research: designing participation to identify and respond to values. Public Adm Rev 72(5):699–708

    Article  Google Scholar 

  43. O’Donnell EL, Horne AC, Godden L, Head B (2019) Cry me a river: building trust and maintaining legitimacy in environmental flows. Australas J Water Resour 23:1–13. https://doi.org/10.1080/13241583.2019.1586058

    Article  Google Scholar 

  44. Ostanello A, Tsoukiàs A (1993) An explicative model of ‘public’ interorganizational interactions. Eur J Oper Res 70:67–82

    Article  Google Scholar 

  45. Patton MQ (2008) Utilization-focused evaluation. Sage publications, London

    Google Scholar 

  46. Peirce CS (1966) Selected writings. Dover Publications, Mineola

  47. Pluchinotta I, Pagano A, Giordano R, Tsoukiàs A (2018) A system dynamics model for supporting decision makers in irrigation water management. J Environ Manag 223:815–824. https://doi.org/10.1016/j.jenvman.2018.06.083

    Article  Google Scholar 

  48. Pluchinotta I, Kazakçi AO, Giordano R, Tsoukiàs A (2019) Design theory for generating alternatives in public policy making. Group Decis Negot 28:341–375

    Article  Google Scholar 

  49. Portoghese I, Agostino D. D, Agostino R, Giordano R, Scardigno A, Apollonio C, Vurro M (2013) An integrated modelling tool to evaluate the acceptability of irrigation constraint measures for groundwater protection Environ Model Softw 46:90–103

    Article  Google Scholar 

  50. Raboun O, Chojnacki E, Duffa C, Rios-Insua D, Tsoukiàs A (2019) Spatial risk assessment in case of multiple nuclear release scenarios. Socio-Economic Plan Sci 72:2019. https://doi.org/10.1016/j.seps.2019.06.006

    Article  Google Scholar 

  51. Rahwan I, Simari GR (eds) (2009) Argumentation in artificial intelligence. Springer, Dordrecht, New York

  52. Richard A, Mayag B, Meinard Y, Talbot F, Tsoukiàs A (2018) How AI could help physicians during their medical consultations: an analysis of physicians’ decision process to develop efficient decision support systems for medical consultations. In: PFIA 2018, Nancy, France

  53. Scott JC (1998) Seeing like a State. Yale University Press, New Haven

    Google Scholar 

  54. The White House (2019) Executive order on maintaining American leadership in artificial intelligence. The White House. https://www.whitehouse.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/. Accessed 8 Mar 2019

  55. Touret R, Meinard Y, Petit J-C, Tsoukias A (2019) Cartographie descriptive du système national français du financement de la recherche sur projet en vue de son évaluation. Innovations 59:205–241

    Article  Google Scholar 

  56. Tsoukias A, Montibeller G, Lucertini G, Belton V (2013) Policy analytics: an agenda for research and practice. EURO J Decis Process 1:115–134

    Article  Google Scholar 

  57. Villani C (2018) For a meaningful artificial intelligence: towards a French and European Strategy. Government of France. https://www.aiforhumanity.fr/pdfs/MissionVillani_Report_ENG-VF.pdf. Accessed 6 Mar 2019

  58. Webster G, Creemers R, Triolo P, Kania E (2019) Full translation: China’s ‘New Generation Artificial Intelligence Development Plan’ (2017). New America. https://www.newamerica.org/cybersecurity-initiative/digichina/blog/full-translation-chinas-new-generation-artificial-intelligence-development-plan-2017/. Accessed 9 Mar 2019

  59. Wenger A, Dunn Cavelty M, Jasper U (2020) The politics and science of the future: assembling future knowledge and integrating it into public policy and governance. In: Wenger A, Jasper U, Dunn Cavelty M (eds) The politics and science of prevision: governing and probing the future. Routledge, Abingdon, New York, p 229–251

Download references

Acknowledgements

This collaborative research was supported by a Grant from the ANU Global Research Partnerships Scheme, and two EU Erasmus+ Jean Monnet projects, the “Europa Policy Labs” and the “Water Policy Innovation Hub”.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yves Meinard.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meinard, Y., Barreteau, O., Boschet, C. et al. What is Policy Analytics? An Exploration of 5 Years of Environmental Management Applications. Environmental Management (2021). https://doi.org/10.1007/s00267-020-01408-z

Download citation

Keywords

  • Decision support
  • Environmental policies
  • Legitimacy
  • Data
  • Policy analytics