¹ N. Savage, ‘Big Data Goes Green’ (2018) 558(7711), Nature, p. S19.

² OECD, Data-Driven Innovation: Big Data for Growth and Well-Being (OECD, 2015), Executive Summary, p. 17, available at: http://dx.doi.org/10.1787/9789264229358-en.

³ See the 2014 Report of the UN Secretary-General's Independent Expert Advisory Group on the Data Revolution for Sustainable Development in ‘A World that Counts’, Nov. 2014, available at: http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf.

⁴ UNEP, ‘The Need for Numbers: Goals, Targets and Indicators for the Environment’, UNEP Global Environmental Alert Service, Mar. 2012, available at: https://unepgrid.ch/storage/app/media/legacy/GEAS_TheNeedforNumbers.pdf.

⁵ UNEP, Keeping Track of Our Changing Environment: From Rio to Rio+20 (1992–2012) (UNEP, 2011), p. vii.

⁶ Global Environmental Goals are defined as ‘internationally agreed environmental goals and objectives, which are part of outcome documents of relevant UN summits and conferences, resolutions of the UN General Assembly, decisions of other global intergovernmental conferences, MEAs and decisions of their governing bodies’: see the Global Environmental Goals (GEGs) Live Tracker, available at: http://geodata.grid.unep.ch/gegslive, and the Environmental Data Explorer, available at: http://geodata.grid.unep.ch.

⁷ UN Environment Assembly (UNEA), Resolution 1/1, Ministerial Outcome Document of the First Session of the UN Environment Assembly of UNEP (26–27 June 2014), available at: https://wedocs.unep.org/bitstream/handle/20.500.11822/17285/K1402364.pdf?sequence=3&isAllowed=y.

⁸ Ashley, K.D., Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age (Cambridge University Press, 2017)CrossRefGoogle Scholar.

⁹ Atias, C., Epistémologie Juridique, coll. Droit Fondamental (Presses Universitaires de France, 1985)Google Scholar cited by Galdia, M., Legal Linguistics (Peter Lang, 2009), pp. 282–3Google Scholar.

¹⁰ See Fisher, E., Environmental Law: A Very Short Introduction (Oxford University Press, 2017), p. 88CrossRefGoogle Scholar.

¹¹ When we refer to science and scientific knowledge here, we mean science built iteratively by the scientific community as a whole (as with the Intergovernmental Panel on Climate Change (IPCC)) and not through the intervention of some appointed experts in a specific arena.

¹² On the role of science in policymaking, see Jasonoff, S., Fifth Branch: Science Advisors as Policymakers (Harvard University Press, 1990), pp. 11–9Google Scholar

¹³ Gutwirth, S., Leenes, R. & de Hert, P. (eds), Data Protection on the Move: Current Development in ICT and Privacy/Data Protection (Springer, 2016)CrossRefGoogle Scholar; Taylor, L., Floridi, L. & van der Sloot, B. (eds), Group Privacy: New Challenges of Data Technologies (Springer, 2017)CrossRefGoogle Scholar; Floridi, L. et al. , ‘AI4People – An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations’ (2018) 28(4) Minds and Machines, pp. 689–707CrossRefGoogle ScholarPubMed; European Economic and Social Committee (EESC), The Ethics of Big Data: Balancing Economic Benefits and Ethical Questions of Big Data in the EU Policy Context (European Union (EU), 2017).

¹⁴ Shelton, A. Kiss & D., Guide to International Environmental Law (Martinus Nijhoff, 2007), p. 20Google Scholar (‘The law of state responsibility requires establishing a link of causality between a culpable act and the damage suffered, and the damage must not be too remote or too speculative’).

¹⁵ Honoré, H.L.A. Hart & T., Causation in the Law (Oxford University Press, 1985)Google Scholar.

¹⁶ In the context of the use of massive data and artificial intelligence (AI), it is interesting to see how AI sciences conceptualize the links between agency, agents’ beliefs, goals and preferences, plans and normative systems: see, e.g., Boella, G., Damiano, L. Lesmo & R., ‘On the Ontological Status of Plans and Norms’ (2004) 12(4) Artificial Intelligence and Law, pp. 317–57CrossRefGoogle Scholar.

¹⁷ Changing this point of view has important consequences for law, as developed in Halpern, Y., Actual Causality (The MIT Press, 2016)CrossRefGoogle Scholar.

¹⁸ Kitchin, R., ‘Big Data, New Epistemologies and Paradigm Shifts’ (2014) 1(1) Big Data & Society, pp. 1–12CrossRefGoogle Scholar.

¹⁹ LeCun, Y., Hinton, Y. Bengio & G., ‘Deep Learning’ (2015) 521(7553) Nature, pp. 436–44CrossRefGoogle ScholarPubMed.

²⁰ Burrell, J., Machine, ‘How the “Thinks”: Understanding Opacity in Machine Learning Algorithms’ (2016) 3(1) Big Data & Society, pp. 1–12Google Scholar.

²¹ Faghmous, J.H. & Kumar, V., ‘A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science’ (2014) 2(3) Big Data, pp. 155–63CrossRefGoogle ScholarPubMed.

²² Fleming, L. et al. , ‘Big Data in Environment and Human Health’, Oxford Encyclopedia of Environment and Human Health (Oxford University Press, 2017)Google Scholar, available at: http://dx.doi.org/10.1093/acrefore/9780199389414.013.541.

²³ Franklin, J. et al. , ‘Big Data for Forecasting the Impacts of Global Change on Plant Communities’ (2016) 26(1) Global Ecology and Biogeography, pp. 6–17CrossRefGoogle Scholar.

²⁴ Environmental Law Institute (ELI), Big Data and Environmental Protection: An Initial Survey of Public and Private Initiatives (ELI, 2014).

²⁵ A. Hsu et al., ‘2016 Environmental Performance Index’, Jan. 2016, Yale University, available at: https://epi2016.yale.edu/sites/default/files/2016EPI_Full_Report_opt.pdf.

²⁶ F. Steves & A. Teytelboym, ‘Political Economy of Climate Change Policy’, Smith School of Enterprise and the Environment Working Paper 13-06, Oct. 2013, available at: http://www.smithschool.ox.ac.uk/publications/wpapers/workingpaper13-06.pdf.

²⁷ P.V. Coveney, E.R. Dougherty & R.R. Highfield, ‘Big Data Need Big Theory Too’ (2016) Philosophical Transactions of the Royal Society online articles, A374: 20160153, available at: https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2016.0153.

²⁸ Wise, A.F. & Shaffer, D.W., ‘Why Theory Matters More Than Ever in the Age of Big Data’ (2015) 2(2) Journal of Learning Analytics, pp. 5–13CrossRefGoogle Scholar.

²⁹ Elmqvist, T. et al. , ‘Response Diversity, Ecosystem Change, and Resilience’ (2003) 1(9) Frontiers in Ecology and the Environment, pp. 488–94CrossRefGoogle Scholar.

³⁰ Weinrib, E.J., The Idea of Private Law (Oxford University Press, 2012)CrossRefGoogle Scholar.

³¹ Luhmann, N., ‘Operational Closure and Structural Coupling: The Differentiation of the Legal System’ (1991) 13(5) Cardozo Law Review, pp. 1419–41Google Scholar.

³² World Economic Forum (WEF) in partnership with PwC and the Stanford Woods Institute for the Environment, ‘Harnessing Artificial Intelligence for the Earth’, WEF Fourth Industrial Revolution for the Earth Series, Jan. 2018, available at: http://www3.weforum.org/docs/Harnessing_Artificial_Intelligence_for_the_Earth_report_2018.pdf. See also CGIAR Platform for Big Data in Agriculture created in 2017 and applying data-driven solutions to ongoing research for development impact, available at: https://bigdata.cgiar.org.

³³ Rio de Janeiro (Brazil), 5 June 1992, in force 29 Dec. 1993, available at: http://www.cbd.int/convention/text.

³⁴ Statement by the Executive Secretary of the CBD, Cristiana Paşca Palmer, at the 22^nd Meeting of the Subsidiary Body on Scientific, Technical and Technological Advice, 2–7 July 2018, Montreal (Canada), p. 2, available at: https://www.cbd.int/doc/speech/2018/sp-2018-07-02-sbstta22-en.pdf.

³⁵ Ripple, W.J. et al. , ‘World Scientists’ Warning to Humanity: A Second Notice’ (2017) 67(12) Bioscience, pp. 1026–8CrossRefGoogle Scholar.

³⁶ Campbell, L.M., Gray, S. Hagerman & N.J., ‘Producing Targets for Conservation: Science and Politics at the Tenth Conference of the Parties to the Convention on Biological Diversity’ (2014) 14(3) Global Environmental Politics, pp. 41–63CrossRefGoogle Scholar. For targets in the Kyoto Protocol see United Nations Framework Convention on Climate Change (UNFCCC), Kyoto Protocol Reference Manual on Accounting of Emissions and Assigned Amount (UNFCCC, 2008).

³⁷ Nicholson, E. et al. , ‘Making Robust Policy Decisions Using Global Biodiversity Indicators’ (2012) 7 PLoS One, e41128CrossRefGoogle ScholarPubMed.

³⁸ UNEP, ‘Measuring Progress: Environmental Goals and Gaps’, UNEP Global Environmental Alert Service, Dec. 2012, available at: https://na.unep.net/geas/archive/pdfs/GEAS_Dec2012_MeasuringProgress.pdf.

³⁹ The choice of these objectives is explained in the report: ibid.

⁴⁰ See, e.g., CBD COP-10, Decision X/2, ‘Strategic Plan for Biodiversity 2011–2020’, Annex 3, para. 12 (‘sound science’).

⁴¹ See, e.g., S.L. Maxwell et al., ‘Being Smart about SMART Environmental Targets’ (2015) 347(6226) Science, pp. 1075–6 (‘International signatories readily agree on targets that are ambiguous in definition because a level of increase or reduction required to meet the target is not clearly specified’ and ‘what is practicable is not defined’, which constitutes a real issue for its implementation at the national level).

⁴² As sharply pointed out by Philippopoulos-Mihalopoulos, ‘[n]o longer can the law barricade itself against other disciplines – and if this is true for law in general, environmental law is arguably the most prominent example of such a change. There is no longer a clear-cut boundary between environmental law and, say, science’: A. Philippopoulos-Mihalopoulos, Law and Ecology: New Environmental Foundations (Routledge, 2011), p. 5. For the various decision-making levels, see International Union for the Conservation of Nature (IUCN), ‘World Declaration on the Environmental Rule of Law’, IUCN World Congress on Environmental Law, Rio de Janeiro (Brazil), 26–29 Apr. 2016, III, available at: https://www.iucn.org/sites/dev/files/content/documents/english_world_declaration_on_the_environmental_rule_of_law_final.pdf (‘Effective implementation is fundamental to achieving the environmental rule of law’. It relies on ‘[m]echanisms to add procedural strength and help build the procedural and substantive components of the environmental rule of law at national, sub-national, regional, and international levels’).

⁴³ CBD Secretariat, ‘Quick Guidelines to the Aichi Biodiversity Targets’, Feb. 2013, available at: https://www.cbd.int/doc/strategic-plan/targets/compilation-quick-guide-en.pdf.

⁴⁴ S.M. Hagerman & R. Pelai, ‘“As Far as Possible and as Appropriate”: Implementing the Aichi Biodiversity Targets’ (2016) 9(6) Conservation Letters, pp. 469–78.

⁴⁵ Policymakers or science-policy interface are rarely defined; nevertheless, there is a wide range of policymakers depending on the level of decision examined. The point should also be questioned.

⁴⁶ See Campbell, Hagerman & Gray, n. 36 above.

⁴⁷ C. Miller, ‘Hybrid Management: Boundary Organizations, Science Policy, and Environmental Governance in the Climate Regime’ (2001) 26(4) Special Issue: Boundary Organizations in Environmental Policy and Science, Science, Technology, & Human Values, pp. 478–500, at 480; see also T.M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public (Princeton University Press, 1995) notably Ch. 8 ‘Objectivity and the Politics of Disciplines’, pp. 206–31.

⁴⁸ Campbell, Hagerman & Gray, n. 36 above.

⁴⁹ S.H.M. Butchart, M. Di Marco & J.E.M. Watson, ‘Formulating Smart Commitments on Biodiversity: Lessons from the Aichi Targets’ (2016) 9(6) Conservation Letters, pp 457–68, at 458.

⁵⁰ D.P. Tittensor et al., ‘A Mid-Term Analysis of Progress toward International Biodiversity Targets’ (2014) 346(6206) Science, pp. 241-4.

⁵¹ CBD COP-6, Decision VI/26, ‘Strategic Plan for the Convention on Biological Diversity’.

⁵² Campbell, Hagerman & Gray, n. 36 above; Butchart, Di Marco & Watson, n. 49 above.

⁵³ See Maxwell et al., n. 41 above.

⁵⁴ In the context of the implementation of One Health approaches to environmental, human and animal health see, e.g., P. Mazzega & C. Lajaunie, ‘Modelling Organisation Networks Collaborating on Health and Environment within ASEAN’, in R.S. Martinez (ed.), Complex Systems: Theory and Applications (Nova, 2017), pp. 117–48.

⁵⁵ J. McEldowney & S. McEldowney, ‘Science and Environmental Law: Collaboration across the Double Helix’ (2011) 13(3) Environmental Law Review, pp. 169–98.

⁵⁶ A.F. Chalmers, What Is This Thing Called Science? An Assessment of the Nature and Status of Science and Its Methods, 2^nd edn (University of Queensland Press, 1982).

⁵⁷ M. Chein & M.-L. Mugnier, Graph-based Knowledge Representation (Springer, 2009), p. 427; S. Katalnikova & L. Novickis, ‘Choice of Knowledge Representation Model for Development of Knowledge Base: Possible Solutions’ (2018) 9(2) International Journal of Advanced Computer Science & Applications, pp. 358–63.

⁵⁸ Science-Metrix: D. Campbell et al., ‘Data Mining: Knowledge and Technology Flows in Priority Domains within the Private Sector and between the Public and Private Sectors’, European Commission Directorate-General for Research and Innovation, Directorate A – Policy Development and Coordination, Specific Contract No. 30-CE-0677881/00-67, Feb. 2017, available at: https://rio.jrc.ec.europa.eu/en/library/data-mining-knowledge-and-technology-flows-priority-domains-within-private-sector-and.

⁵⁹ T. Balke & N. Gilbert, ‘How Do Agents Make Decisions? A Survey’ (2014) 17(4) Journal of Artificial Societies and Social Simulation online articles, p. 13, available at: http://jasss.soc.surrey.ac.uk/17/4/13.html.

⁶⁰ H. Aldewereld et al. (eds), Social Coordination Frameworks for Social Technical Systems (Springer, 2016); X.A. Ghose et al. (eds), Coordination, Organizations, Institutions, and Norms in Agent Systems. LNAI 9372 (Springer, 2014); M. Janssen, M.A. Wimmer & A. Deljoo (eds), Policy Practice and Digital Science (Springer, 2015).

⁶¹ See, e.g., International Social Science Council (ISSC) and UNESCO, World Social Science Report 2013: Changing Global Environments (OECD and UNESCO, 2013), pp. 22 et seq.

⁶² L. Lessig, Code: Version 2.0 (Basic Books, Perseus Group, 2006), p. 4.

⁶³ McEldowney & McEldowney, n. 55 above, p. 189.

⁶⁴ Conseil d'Etat, Rapport public 2006: Jurisprudence et avis de 2005 – Sécurité juridique et complexité du droit, Etudes et documents No. 57 (Conseil d'Etat Paris, 2006).

⁶⁵ R.A. Epstein, Simple Rules for a Complex World (Harvard University Press, 1995).

⁶⁶ P. Schuck, ‘Legal Complexity: Some Causes, Consequences, and Cures’ (1992) 42(1) Duke Law Journal, pp. 1–52.

⁶⁷ P. Casanovas et al. (eds), AI Approaches to the Complexity of Legal Systems: Complex Systems, the Semantic Web, Ontologies, Argumentation and Dialogue (Springer, 2009).

⁶⁸ N. Bobbio, L’étà dei diritti (G. Einaudi ed., Torina 1997), pp. xiii–xv.

⁶⁹ This type of right would probably be included by Bobbio (ibid., p. xiv) into the ‘fourth generation rights’ together with rights induced by uses of genetic heritage of ‘each specific individual’.

⁷⁰ European Parliament, ‘Report with Recommendations to the Commission on Civil Law Rules on Robotics’, 2015/2103(INL), 27 Jan. 2017.

⁷¹ K. Kittichaisaree, Public International Law of Cyberspace (Springer, 2017).

⁷² D. Bourcier & P. Mazzega, ‘Toward Measures of Legal Complexity in Legal Systems’ in Proceedings of the 11^th International Conference on Artificial Intelligence and Law, Stanford, CA (US), 4–8 June 2007, pp. 211–15, available at: https://dl.acm.org/citation.cfm?id=1276359.

⁷³ In a complex system, a same cause may have different effects depending on the state of the system, and the kind of causality link. The predictability of the changes is limited to a finite horizon of prediction intrinsic to the system dynamic.

⁷⁴ J.B. Ruhl & D.M. Katz, ‘Measuring, Monitoring, and Managing Legal Complexity’ (2015) 101 Iowa Law Review, pp. 191–244.

⁷⁵ P. Mazzega, D. Bourcier & R. Boulet, ‘The Network of French Legal Codes’, Proceedings of the 12^th International Conference on Artificial Intelligence and Law, Barcelona (Spain), 8–12 June 2009, pp. 236–37, available at: https://dl.acm.org/citation.cfm?doid=1568234.1568271; D.M. Katz & M. Bommarito, ‘Measuring the Complexity of the Law: The United States Code’ (2014) 22(4) Artificial Intelligence and Law, pp. 337–74.

⁷⁶ Casanovas et al., n. 67 above; G. Sartor et al. (eds), Approaches to Legal Ontologies (Springer, 2011).

⁷⁷ J.H. Fowler et al., ‘Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court’ (2007) 15(3) Political Analysis, pp. 324–46.

⁷⁸ R. Boulet, A.F. Barros-Platiau & P. Mazzega, ‘35 Years of Multilateral Environmental Agreements Ratification: A Network Analysis’ (2016) 24(2) Artificial Intelligence and Law, pp. 133–48; R. Boulet, A.F. Barros-Platiau & P. Mazzega, ‘Environmental and Trade Regimes: Comparison of Hypergraphs Modelling the Ratifications of UN Multilateral Treaties’, in R. Boulet, C. Lajaunie & P. Mazzega (eds), Law, Public Policies and Complex Systems: Networks in Action (Springer, 2019), pp. 221–42.

⁷⁹ The role of the IPCC is to assess the thousands of scientific papers published each year to inform policymakers about the risks related to climate change. It thus identifies where there is agreement in the scientific community, where there are differences of opinion, and where further research is needed, available at: https://www.ipcc.ch/organization/organization.shtml.

⁸⁰ IPCC, ‘Appendix A to the Principles Governing IPCC Work: Procedures for the Preparation, Review, Acceptance, Adoption, Approval and Publication of IPCC Reports’, Oct. 2013, para. 4.3.4, available at: https://www.ipcc.ch/site/assets/uploads/2018/09/ipcc-principles-appendix-a-final.pdf.

⁸¹ J.C. Minx et al., ‘Learning about Climate Change Solutions in the IPCC and Beyond’ (2017) 77(C) Environmental Science & Policy, pp. 252–9.

⁸² The relevant literature to be reviewed for the IPCC's sixth assessment will include between 270,000 and 330,000 publications. This is larger than the entire climate change literature before 2014. Cf Minx et al., n. 81 above.

⁸³ The consultation of contributions to the Journal of Artificial Societies and Social Simulation, for example, gives an overview of the topics discussed and the progress made, available at: http://jasss.soc.surrey.ac.uk/JASSS.html.

⁸⁴ S. Ossowski, Co-ordination in Artificial Agent Societies: Social Structures and Its Implications for Autonomous Problem-Solving Agents (Springer-Verlag, 1999).

⁸⁵ This was partly because the available computational capacity meant that the scenarios that were analyzed remained on the level of ‘toy examples’ or thought experiments, heavily simplified to test specific hypotheses about the way in which external incentives, disincentives and individual planning interact.

⁸⁶ So, canonically, T.J. Bench-Capon & F.P. Coenen, ‘Isomorphism and Legal Knowledge-based Systems’ (1992) 1(1) Artificial Intelligence and Law, pp. 65–86.

⁸⁷ A classic example from that time: see M.J. Sergot et al., ‘The British Nationality Act as a Logic Program’ (1986) 29(5) Communications of the ACM, pp. 370–86 (e.g., a set of rules that determine if someone is eligible for citizenship).

⁸⁸ For a discussion of the problems of legislative intervention in networked, complex environments see, e.g., A.Guadamuz-González, ‘Scale-Free Law: Network Science and Copyright’ (2006) 70 Albany Law Review, pp. 1297–322.

⁸⁹ J. Zeleznikow, A. Stranieri & M. Gawler, ‘Project Report: Split-Up – A Legal Expert System which Determines Property Division upon Divorce’ (1995) 3(4) Artificial Intelligence and Law, pp. 267–75.

⁹⁰ S. Danziger, J. Levav & L. Avnaim-Pesso, ‘Extraneous Factors in Judicial Decisions’ (2011) 108(17) Proceedings of the National Academy of Sciences, pp. 6889–92.

⁹¹ L. Lessig, ‘Law Regulating Code Regulating Law’ (2003) 35(1) Loyola University Chicago Law Journal, pp. 1–14.

⁹² M. Hildebrandt & B.J. Koops, ‘The Challenges of Ambient Law and Legal Protection in the Profiling Era’ (2010) 73(3) The Modern Law Review, pp. 428–60.

⁹³ C. Devins et al., ‘The Law and Big Data’ (2017) 27(3) Cornell Journal of Law and Public Policy, pp. 357–413, at 357.

⁹⁴ J.R. Gil-Garcia, ‘Towards a Smart State? Inter-Agency Collaboration, Information Integration, and Beyond’ (2012) 17(3) Information Polity, pp. 269–80, at 276; W.B. Boyd, ‘Environmental Law, Big Data, and the Torrent of Singularities’ (2016) 64 UCLA Law Review, pp. 544–70, at 544.

⁹⁵ Report of the World Commission on Environment and Development, Our Common Future (United Nations and Oxford University Press, 1987) (Brundtland Report), p. 330.

⁹⁶ With the creation respectively of the Joint Liaison Group and of the Biodiversity Liaison Group.

⁹⁷ UN Environment, Strengthening the Science-Policy Interface: A Gap Analysis (UNEP, 2017).

⁹⁸ In modelling language, an instance is ‘a concrete manifestation of an abstraction’: J. Booch, J. Rumbaugh & I. Jacobson, The Unified Modelling Language User Guide, 2^nd edn (Addison-Wesley, 2005). Instantiation is the process of associating such concrete manifestation (e.g., specific targets for a particular context, a set of empirical data, etc.) with the abstraction (e.g., the generally defined target, the corresponding general type of data, etc.).

⁹⁹ When it comes to the implementation of generic international norms into national law, the environmental or ecological context should be taken into account. For instance, in the case of Good Environmental Status in the European Union (EU), ‘descriptors’ are detailed to help Member States in implementing a Directive, as in the case of the Marine Strategy Framework Directive adopted on 17 June 2008 (Directive 2008/56/EC establishing a Framework for Community Action in the Field of Marine Environmental Policy, [2008] OJ L 164/19). The European Commission has also produced a set of detailed criteria and methodological standards to help Member States, which have been modified by Commission Decision (EU) 2017/848 of 17 May 2017, [2017] OJ L 125/43. Interestingly, preambular para. 20 of the Decision states: ‘Criteria, including threshold values, methodological standards, specifications and standardised methods for monitoring and assessment should be based on the best available science. However, additional scientific and technical progress is still required to support the further development of some of them, and should be used as the knowledge and understanding become available’. The European Commission translates the generic character of the norm into criteria which help to instantiate the norm at the national level according to the specific socio-ecological context.

¹⁰⁰ Lajaunie, S. Morand & C., Biodiversity and Health: Linking Life, Ecosystems and Societies (Elsevier ISTE Press, 2017)Google Scholar, Ch. 13 ‘The Role of Law, Justice and Scientific Knowledge in Health and Biodiversity’, pp. 209–18.

¹⁰¹ See Campbell, Hagerman & Gray, n. 36 above.

¹⁰² Newton, A.C., ‘Implications of Goodhart's Law for Monitoring Global Biodiversity Loss’ (2011) 4(4) Conservation Letters, pp. 264–68CrossRefGoogle Scholar.

¹⁰³ UN Environment, n. 97 above.

¹⁰⁴ See, e.g., van den Hoven, J., ‘Moral Methodology and Information Technology’, in Himma, K.E. & Tavani, H.T. (eds), The Handbook of Information and Computer Ethics (Wiley, 2008), pp. 49–67CrossRefGoogle Scholar; Anderson, M. & Anderson, S. Leigh (eds), Machine Ethics (Cambridge University Press, 2011)CrossRefGoogle Scholar; European Commission, High-Level Expert Group on Artificial Intelligence, ‘Draft Ethics Guidelines for Trustworthy AI’, Apr. 2019, available at: https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

¹⁰⁵ For environmental knowledge as well as legal knowledge see the analysis of general knowledge versus particular knowledge and the way to apply a rule of ‘local and situated knowledge’ in settings that are mutable, undetermined (some facts are unknown), and particular: see Scott, J.C., Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (Yale University Press, 1998), pp. 311–21Google Scholar.

¹⁰⁶ Tuana, N., ‘Leading with Ethics, Aiming for Policy: New Opportunities for Philosophy of Science’ (2010) 177(3) Synthese, pp. 471–92CrossRefGoogle Scholar, available at: https://doi.org/10.1007/s11229-010-9793-4. For a study of the ethical side of interdisciplinary research on the environment cf. C. Lajaunie & P. Mazzega (invited eds), ‘The Ethics of Biodiversity Conservation’ (2018) 10(4) Asian Bioethics Review, Special Issue.

¹⁰⁷ Hildebrandt & Koops, n. 92 above, p. 428. See also Leenes, R., ‘Framing Techno-Regulation: An Exploration of State and Non-State Regulation by Technology’ (2011) 5(2) Legisprudence, pp. 143–69CrossRefGoogle Scholar.

¹⁰⁸ Koops, B.J., ‘Law, Technology, and Shifting Power Relations’ (2010) 25(2) Berkeley Technology Law Journal, pp. 973–1035Google Scholar; Hildebrandt, M., ‘Legal Protection by Design: Objections and Refutations’ (2011) 5(2) Legisprudence, pp. 223–48CrossRefGoogle Scholar.

¹⁰⁹ See, in particular, Wahlgren, P. (ed.), A Proactive Approach (Stockholm Institute for Scandinavian Law, 2006)Google Scholar; H. Haapio (ed.), A Proactive Approach to Contracting and Law (International Association for Contract and Commercial Management and Turku University of Applied Sciences, 2008).

¹¹⁰ Preliminary Draft Opinion dated 14 May 2008 of the European Economic and Social Committee (EESC) on ‘The Proactive Law Approach: A Further Step towards Better Regulation at EU Level’, EESC INT/415.

¹¹¹ Shrivastava, G. Berger-Walliser & P., ‘Beyond Compliance: Sustainable Development, Business, and Proactive Law’ (2014) 46 Georgetown Journal of International Law, pp. 417–74Google Scholar.

¹¹² For examples see, e.g., Winkels, R. & Boer, A., ‘Finding and Visualizing Dutch Legislative Context Networks’ (2014) 3 Diritto, Scienza, Tecnologia, pp. 157–82Google Scholar; Boulet, R., Mazzega, P. & Bourcier, D., ‘Network Approach to the French System of Legal Codes, Part II: The Role of the Weights in a Network’ (2018) 26(1) Artificial Intelligence and Law, pp. 23–47CrossRefGoogle Scholar.