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A review of data-intensive approaches for sustainability: methodology, epistemology, normativity, and ontology

  • Vivek Anand AsokanEmail author
  • Masaru Yarime
  • Motoharu Onuki
Review Article
Part of the following topical collections:
  1. Concepts, Methodology, and Knowledge Management for Sustainability Science

Abstract

With the growth of data, data-intensive approaches for sustainability are becoming widespread and have been endorsed by various stakeholders. To understand their implications, in this paper, data-intensive approaches for sustainability will be explored by conducting an extensive review. The current data-intensive approaches are defined as an amalgamation of traditional data-collection methods, such as surveys and data from monitoring networks, with new data-collection methods that involve new information communication technology. Based on a comprehensive review of the current data-intensive approaches for sustainability, key challenges are identified: the lack of data availability, diverse indicators developed from a narrowly viewed base, diverse definitions and values, skewed global representation, and the lack of social and economic information collected, especially among the business indicators. To clarify the implications of these trends, four major research assumptions regarding data-intensive approaches are elaborated: the methodology, epistemology, normativity, and ontology. Caution is required when data-intensive approaches are masked as “objective”. Overcoming this issue requires interdisciplinary and community-based approaches that can offer ways to address the subjectivities of data-intensive approaches. The current challenges to interdisciplinarity and community-based approaches are also identified, and possible solutions are explored, so that researchers can employ them to make the best use of data-intensive approaches.

Keywords

Data-intensive approaches Sustainability Sustainability indicators SDGs Planetary boundary Open data Big data 

Notes

Acknowledgements

We acknowledge Monbukagakusho (MEXT), the Government of Japan, for their financial assistance during the doctoral study. We would like to thank the anonymous reviewers and editors for providing new insights and broadening the scope of the paper. We would like to especially thank Nikole Roland and Clare Sandford for providing editing and proofreading assistance.

References

  1. Agrawal A (1995) Dismantling the divide between indigenous and scientific knowledge. Dev Change 26(3):413–439CrossRefGoogle Scholar
  2. Anjum RL, Copeland S, Rocca E (2018) Medical scientists and philosophers worldwide appeal to EBM to expand the notion of ‘evidence’. BMJ Evid Based Med (bmjebm-2018) Google Scholar
  3. Ansell CK, Bartenberger M (2016) Varieties of experimentalism. Ecol Econ 130:64–73CrossRefGoogle Scholar
  4. Asokan VA, Yarime M, Esteban M (2017) Introducing flexibility to complex, resilient socio-ecological systems: a comparative analysis of economics, flexible manufacturing systems, evolutionary biology, and supply chain management. Sustainability 9(7):1091CrossRefGoogle Scholar
  5. Asokan VA, Yarime M, Onuki M (2019) Bridging practices, institutions, and landscapes through a scale-based approach for research and practice: a case study of a business association in South India. Ecol Econ 160:240–250CrossRefGoogle Scholar
  6. Association of Chartered Certified Accountants (2013) Big data: its power and perils. http://www.accaglobal.com/bigdata
  7. Autodesk Inc (2016) Autodesk Inc. http://sustainability.autodesk.com/available-solutions/c-fact/. Retrieved 10 Jan 2016, from Corporate and city sustainability —C-FACT
  8. Beck S (2011) Moving beyond the linear model of expertise? IPCC and the test of adaptation. Reg Environ Change 11(2):297–306CrossRefGoogle Scholar
  9. Berger JO, Berry DA (1988) Statistical analysis and the illusion of objectivity. Am Sci 76(2):159–165Google Scholar
  10. Bjørn A, Hauschild MZ (2015) Introducing carrying capacity-based normalisation in LCA: framework and development of references at midpoint level. Int J Life Cycle Assess 20(7):1005–1018CrossRefGoogle Scholar
  11. Bjørn A, Bey N, Georg S, Røpke I, Hauschild MZ (2017) Is Earth recognized as a finite system incorporate responsibility reporting? J Clean Prod 163:106–117.  https://doi.org/10.1016/j.jclepro.2015.12.095 CrossRefGoogle Scholar
  12. Blunt C (2015) Hierarchies of evidence in evidence-based medicine. PhD thesis, The London School of Economics and Political Science (LSE)Google Scholar
  13. Böhringer C, Jochemc PE (2016) Measuring the immeasurable—a survey of sustainability indices. ftp://ftp.zew.de/pub/zew-docs/dp/dp06073.pdf. Retrieved Feb 2016 from ZEW—discussion paper no. 06-073Google Scholar
  14. Bond AJ, Morrison-Saunders A (2011) Re-evaluating sustainability assessment: aligning the vision and the practice. Environ Impact Assess Rev 31(1):1–7CrossRefGoogle Scholar
  15. Bond AJ, Morrison-Saunders A, Pope J (2012) Sustainability assessment: the state of the art. Impact Assess Project Appraisal 30(1):53–62CrossRefGoogle Scholar
  16. Boulanger PM (2014) Elements for a comprehensive assessment of public indicators. In: Report procured by the European Commission-Joint Research Centre, Econometrics and Applied Statistics (DDG 01)Google Scholar
  17. Brown B, Chui M, Manyika J (2011) Are you ready for the era of ‘big data’. McKinsey Q 4(1):24–35Google Scholar
  18. BTplc (2016) Climate stabilisation intensity targets. https://www.btplc.com/Betterfuture/NetGood/OurNetGoodgoal/OurCSIMethodology/CSI_Methodology.pdf. Retrieved Jan 2016 from BTplc
  19. Bulkeley H, Castán Broto V (2013) Government by experiment? Global cities and the governing of climate change. Trans Inst Br Geogr 38(3):361–375CrossRefGoogle Scholar
  20. Busch L (2014) Big data, big questions| a dozen ways to get lost in translation: inherent challenges in large scale data sets. Int J Commun 8:18Google Scholar
  21. Bjørn A, Diamond M, Owsianiak M, Verzat B, Hauschild, MZ (2015) Strengthening the link between life cycle assessment and indicators for absolute sustainability to support development within planetary boundariesGoogle Scholar
  22. Callebaut W (2012) Scientific perspectivism: a philosopher of science’s response to the challenge of big data biology. Stud Hist Philos Sci Part C Stud Hist Philos Biol Biomed Sci 43(1):69–80CrossRefGoogle Scholar
  23. Campbell DT (1979) Assessing the impact of planned social change. Eval Program Plan 2(1):67–90CrossRefGoogle Scholar
  24. Caniglia G, Schäpke N, Lang DJ, Abson DJ, Luederitz C, Wiek A, Laubichler MD, Gralla F, von Wehrden H (2017) Experiments and evidence in sustainability science: a typology. J Clean Prod 169:39–47CrossRefGoogle Scholar
  25. Carbon Disclosure Project (2016) Reporting to CDP. https://www.cdp.net/en-US/Respond/Pages/companies.aspx. Retrieved Jan 2016 from Carbon Disclosure Project
  26. Cartwright N, Hardie J (2012) Evidence-based policy: a practical guide to doing it better. Oxford University Press, OxfordCrossRefGoogle Scholar
  27. Cash DW, Clark W, Alcock F, Dickson NM, Eckley N, Jaeger J (2004) Salience, credibility, legitimacy and boundaries: linking research, assessment and decision making. Global Environmental Assessment Project, Harvard University (http://www.ksg.harvard.edu/gea)
  28. Center for Open Data Enterprise (2016) The open data impact map. http://opendataenterprise.org/index.html
  29. Center for Strategic and International Studies (CSIS), Japan International Cooperation Agency (JICA) Research Institute (2017) Harnessing the data revolution to achieve the sustainable development goals: enabling frogs to leap. Rowman & Littlefield, LanhamGoogle Scholar
  30. Ceres (2016) Global initiative for sustainability ratings (GISR). http://ratesustainability.org/hub/index.php/search/
  31. Ceres and Sustainalytics (2012) The road to 2020: corporate progress on the Ceres roadmap for sustainability. Ceres and Sustainalytics, BostonGoogle Scholar
  32. Clark WC (2007) Sustainability science: a room of its own. PNAS 104(6):1737–1738.  https://doi.org/10.1073/pnas.0611291104 CrossRefGoogle Scholar
  33. Clark WC, Van Kerkhoff L, Lebel L, Gallopin GC (2016) Crafting usable knowledge for sustainable development. Proc Natl Acad Sci 113(17):4570–4578CrossRefGoogle Scholar
  34. Climate Disclosure Standards Board (2016) CDSB framework for reporting environmental information & natural capital. http://www.cdsb.net/what-we-do/reporting-frameworks/environmental-information-natural-capital. Retrieved 10 Jan 2016 from Climate Disclosure Standards Board
  35. Colander D (2005) The making of an economist redux. J Econ Perspect 19(1):175–198CrossRefGoogle Scholar
  36. Cole MJ, Bailey RM, New MG (2014) Tracking sustainable development with a national barometer for South Africa using a downscaled “safe and just space” framework. Proc Natl Acad Sci 111(42):E4399–E4408CrossRefGoogle Scholar
  37. Cooksey R, McDonald G (2011) Surviving and thriving in postgraduate research. VIC, Tilde University Press, PrahranGoogle Scholar
  38. Cornell S, Downing A (2004) Environment, absolute? The quality infrastructure of the planetary boundaries. Discussion paper. Physikalisch-Technische Bundesanstalt, BraunschweigGoogle Scholar
  39. Cort T (2015) Vanishing materiality in sustainability reporting. Environmental leader. https://www.environmentalleader.com/2015/09/vanishing-materiality-in-sustainability-reporting/
  40. Craver C, Tabery J (2017) Mechanisms in Science. In: Zalta EN (ed) The Stanford encyclopedia of philosophy. Springer, New York. https://plato.stanford.edu/archives/spr2017/entries/science-mechanisms/
  41. Crawford K (2013) The hidden biases in big data. Harv Bus Rev 1Google Scholar
  42. Crawford K, Schultz J (2014) Big data and due process: toward a framework to redress predictive privacy harms. BCL Rev 55:93Google Scholar
  43. Dalin C, Wada Y, Kastner T, Puma MJ (2017) Groundwater depletion embedded in international food trade. Nature 543(7647):700CrossRefGoogle Scholar
  44. Dao H, Peduzzi P, Chatenoux B, Bono AD, Schwarzer S, Friot D (2015) Environmental limits and Swiss footprints based on planetary boundaries. Swiss Federal Office for the Environment (FOEN)Google Scholar
  45. Davenport TH (2009) How to design smart business experiments. Strateg Direct 25(8)Google Scholar
  46. Davenport TH (2015) It’s way too late not to know where your data is. Wall Street J. http://www.tomdavenport.com/wp-content/uploads/Its-Way-Too-Late-Not-to-Know-Where-Your-Data-Is.pdf
  47. Davidian M, Louis TA (2012) Why statistics? Science 336(6077):1–12.  https://doi.org/10.1126/science.1218685 CrossRefGoogle Scholar
  48. Deaton A (2010) Understanding the mechanisms of economic development. J Econ Perspect 24(3):3–16CrossRefGoogle Scholar
  49. Derman E (2011) Models behaving badly: why confusing illusion with reality can lead to disaster, on wall street and in life. Simon and Schuster, New YorkGoogle Scholar
  50. DJSI annual review 2015 (2015) http://www.sustainability-indices.com/. Retrieved 7 Jan 2016 from ROBECOSAM
  51. Dunning C, Jared K (2016) ‘What SDGs can we track now?’ Center for Global Development. https://www.cgdev.org/blog/what-sdgs-can-we-track-now. Accessed 16 Sep 2018
  52. Eccles RG, Ioannou I, Serafeim G (2014) The impact of corporate sustainability on organizational processes and performance. Manag Sci 60(11):2835–2857CrossRefGoogle Scholar
  53. Edwards PN, Mayernik MS, Batcheller AL, Bowker GC, Borgman CL (2011) Science friction: data, metadata, and collaboration. Soc Stud Sci 41(5):667–690CrossRefGoogle Scholar
  54. Elster J (1989) Nuts and bolts for the social sciences. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  55. Fairfield J, Shtein H (2014) Big data, big problems: emerging issues in the ethics of data science and journalism. J Mass Media Ethics 29(1):38–51CrossRefGoogle Scholar
  56. Fitz-Gibbon CT (1990) Performance indicators, vol 2. Multilingual Matters, BristolGoogle Scholar
  57. Forscher BK (1963) Chaos in the brickyard. Science 142(3590):339CrossRefGoogle Scholar
  58. Fourcade M, Ollion E, Algan Y (2015) The superiority of economists. J Econ Perspect 29(1):89–114.  https://doi.org/10.1257/jep.29.1.89 CrossRefGoogle Scholar
  59. FTSE4Good Index Series (2016) http://www.ftse.com/products/indices/FTSE4Good. Retrieved 8 Jan 2016 from FTSE Russell
  60. Galli A, Wiedmann TO, Ercin E, Knoblauch D, Ewing BR, Giljum S (2011) Integrating ecological, carbon and water footprint: defining the footprint family and its application in tracking human pressure on the planet. Open-EUGoogle Scholar
  61. Gallopin GC (1996) Environmental and sustainability indicators and the concept of situational indicators. A systems approach. Environ Model Assess 1(3):101–117CrossRefGoogle Scholar
  62. Gasparatos A (2010) Embedded value systems in sustainability assessment tools and their implications. J Environ Manag 91(8):1613–1622CrossRefGoogle Scholar
  63. Gasparatos A, Scolobig A (2012) Choosing the most appropriate sustainability assessment tool. Ecol Econ 80:1–7CrossRefGoogle Scholar
  64. Giampietro M, Aspinall RJ, Ramos-Martin J, Bukkens SGF (2014) Resource accounting for sustainability assessment: the nexus between energy, food, water and land use. Taylor & Francis (Routledge explorations in sustainability and governance)Google Scholar
  65. Gilman K, Schulschenk J (2013) Sustainability accounting standards board by Ernst & YoungGoogle Scholar
  66. Gitelman L (2013) Raw data is an oxymoron. MIT Press, New YorkCrossRefGoogle Scholar
  67. Global Pulse (2018) Global pulse. https://www.unglobalpulse.org/jakarta
  68. Global Reporting Initiative (2015) G4-GRI sustainability reporting guidelines. https://www.globalreporting.org/resourcelibrary/GRIG4-Part1-Reporting-Principles-and-Standard-Disclosures.pdf. Retrieved Jan 2016 from Global Reporting Initiative
  69. Goldenberg MJ (2005) Evidence-based ethics? On evidence-based practice and the” empirical turn” from normative bioethics. BMC Med Ethics 6(1):11CrossRefGoogle Scholar
  70. Grantham TA (2004) Conceptualizing the (dis) unity of science. Philos Sci 71(2):133–155CrossRefGoogle Scholar
  71. Gray R, Bebbington J (2005) Corporate sustainability, accountability and the pursuit of the impossible dream. In: Handbook of sustainable development, pp 376–394Google Scholar
  72. Greenhalgh T (1997) How to read a paper: getting your bearings (deciding what the paper is about). BMJ 315(7102):243–246CrossRefGoogle Scholar
  73. Greenhalgh T, Howick J, Maskrey N (2014) Evidence based medicine: a movement in crisis? BMJ 348:g3725CrossRefGoogle Scholar
  74. Grenier L (1998) Working with indigenous knowledge: a guide for researchers. IDRC, OttawaGoogle Scholar
  75. Grimmer J (2015) We are all social scientists now: how big data, machine learning, and causal inference work together. PS Polit Sci Polit 48(1):80–83CrossRefGoogle Scholar
  76. Gudmundsson H (2003) The policy use of environmental indicators-learning from evaluation research. J Transdiscip Environ Stud 2(2):1–12Google Scholar
  77. Guzman K (2015) A good socially responsible investment (SRI) fund is hard to find. Yale School of Management. https://som.yale.edu/blog/2015/09/good-socially-responsible-investment-sri-fund-hard-find. Accessed 14 Sep 2015
  78. Hazas M, Morley J, Bates O, Friday A (2016) Are there limits to growth in data traffic?: on time use, data generation and speed. In: Proceedings of the second workshop on computing within limits. ACM, New York, p. 14Google Scholar
  79. Head BW (2010) Reconsidering evidence-based policy: key issues and challengesCrossRefGoogle Scholar
  80. Heath T, Bizer C (2011) Linked data: evolving the web into a global data space. Synth Lect Semant Web Theory Technol 1(1):1–136CrossRefGoogle Scholar
  81. Hedström P, Ylikoski P (2010) Causal mechanisms in the social sciences. Annu Rev Sociol 36CrossRefGoogle Scholar
  82. Hoffman AJ (2015) How culture shapes the climate change debate. Stanford University Press, StanfordGoogle Scholar
  83. Horton R (2015) Offline: what is medicine’s 5 sigma. Lancet 385(9976):1380CrossRefGoogle Scholar
  84. House of Commons, Parliamentary Office of Science and Technology (2011) Evidence-based conservation: Post Note 379. Parliamentary Office of Science and Technology, Houses of Parliament, LondonGoogle Scholar
  85. IAEG-SDG (2015) IAEG-SDG open consultation on green indicators. Bangkok. https://unstats.un.org/sdgs/iaeg-sdgs/open-consultation-2
  86. Imhoff ML, Bounoua L, Ricketts T, Loucks C, Harriss R, Lawrence WT (2004) Global patterns in human consumption of net primary production. Nature 429(6994):870CrossRefGoogle Scholar
  87. Independent Expert Advisory Group on a Data Revolution for Sustainable Development (2014) A world that counts-mobilising the data revolution for sustainable development. United Nations, New YorkGoogle Scholar
  88. Integrated Reporting Council (‘The IIRC’) (2013) The International <IR> Framework. http://integratedreporting.org/wp-content/uploads/2015/03/13-12-08-THE-INTERNATIONAL-IR-FRAMEWORK-2-1.pdf. Retrieved 10 Jan 2016
  89. Intergovernmental Panel on Climate Change (2018) Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5 °C. An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. World Meteorological Organization, GenevaGoogle Scholar
  90. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer, New YorkCrossRefGoogle Scholar
  91. Jantsch E (1970) Inter- and transdisciplinary university: a systems approach to education and innovation. High Educ 1:7–37Google Scholar
  92. Jerneck A, Olsson L, Ness B, Anderberg S, Baier M, Clark E, Hickler T, Hornborg A, Kronsell A, Lövbrand E, Persson J (2011) Structuring sustainability science. Sustain Sci 6(1):69–82CrossRefGoogle Scholar
  93. Joint Research Centre-European Commission (JRC-EC) (2008) Handbook on constructing composite indicators: methodology and user guide. OECD Publishing, ParisGoogle Scholar
  94. Kareiva PM, McNally BW, McCormick S, Miller T, Ruckelshaus M (2015) Improving global environmental management with standard corporate reporting. Proc Natl Acad Sci 112(24):7375–7382CrossRefGoogle Scholar
  95. Evans J, Karvonen, A (2011) Living laboratories for sustainability: exploring the politics and epistemology of urban transition. In: Cities and low carbon transitions, pp 126–141Google Scholar
  96. Kates RW (2011) What kind of a science is sustainability science? Proc Natl Acad Sci 108(49):19449–19450.  https://doi.org/10.1073/pnas.1116097108 CrossRefGoogle Scholar
  97. Keeso A (2014) Big data and environmental sustainability: a conversation starter. Smith School of Enterprise and the Environment. In: Working paper series (14-04)Google Scholar
  98. Keeves JP (1997) Educational research methodology and measurement. Cambridge University Press, CambridgeGoogle Scholar
  99. Kim SJ, Kara S (2014) Predicting the total environmental impact of product technologies. CIRP Ann Manuf Technol 63(1):25–28CrossRefGoogle Scholar
  100. Komiyama H, Takeuchi K (2006) Sustainability science: building a new disciplineGoogle Scholar
  101. Lang DJ, Wiek A, Bergmann M, Stauffacher M, Martens P, Moll P, Swilling M, Thomas CJ (2012) Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustain Sci 7(1):25–43CrossRefGoogle Scholar
  102. Lazar N (2012) The big picture: big data hits the big time. Chance 25(3):47–49CrossRefGoogle Scholar
  103. Leisinger KM, Bakker MP (2013) The key challenges to 2030/2050: mapping out long-term pathways to sustainability and highlighting solutions that should be scaled up. United Nation Sustainable Development Sustainability NetworkGoogle Scholar
  104. Lucas RE Jr (1988) On the mechanics of economic development. J Monet Econ 22(1):3–42CrossRefGoogle Scholar
  105. Ludwig J, Kling JR, Mullainathan S (2011) Mechanism experiments and policy evaluations. J Econ Perspect 25(3):17–38CrossRefGoogle Scholar
  106. Lydenberg SD, Rogers J, Wood D (2010) From transparency to performance: industry-based sustainability reporting on key issues. Hauser Center for Nonprofit Organizations, CambridgeGoogle Scholar
  107. Madnick S, Siegel M (2002) Seizing the opportunity: exploiting. Web aggregators. MIS Q Exec 1(1):35–46Google Scholar
  108. Marland G, Kowalczyk T, Cherry TL (2015) Green fluff”? The role of corporate sustainability initiatives in effective climate policy: Comment on “Science-based carbon targets for the corporate world: the ultimate sustainability commitment, or a costly distraction? J Ind Ecol 19(6):934–936CrossRefGoogle Scholar
  109. Mazzocchi F (2015) Could Big Data be the end of theory in science?: a few remarks on the epistemology of data-driven science. EMBO Rep 16(10):1250–1255CrossRefGoogle Scholar
  110. McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68Google Scholar
  111. MELODIES Project (2016) MELODIES project. http://www.melodiesproject.eu/. Retrieved 10 Jan 2016 from MELODIES project
  112. Mihelcic JR, Crittenden JC, Small MJ, Shonnard DR, Hokanson DR, Zhang Q, Chen H, Sorby SA, James VU, Sutherland JW, Schnoor JL (2003) Sustainability science and engineering: the emergence of a new metadiscipline. Environ Sci Technol 37(23):5314–5324CrossRefGoogle Scholar
  113. Miller TR, Wiek A, Sarewitz D, Robinson J, Olsson L, Kriebel D, Loorbach D (2014) The future of sustainability science: a solutions-oriented research agenda. Sustain Sci 9(2):239–246CrossRefGoogle Scholar
  114. Milne MJ, Gray R (2013) W (h) ither ecology? The triple bottom line, the global reporting initiative, and corporate sustainability reporting. J Bus Ethics 118(1):13–29CrossRefGoogle Scholar
  115. Mittelstadt BD, Floridi L (2016) The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics 22(2):303–341CrossRefGoogle Scholar
  116. Moir E, Moonen T, Clark G (2014) What are future cities? Origins, meanings and uses (PDF). Foresight future of cities project and future cities CatapultGoogle Scholar
  117. Moran D, Kanemoto K (2017) Identifying species threat hotspots from global supply chains. Nat Ecol Evol 1(1):0023CrossRefGoogle Scholar
  118. Narasimhan M, Arun A (2017) Shifting gears: randomised control trials and the future of development evaluation. South Asia@ LSE, LondonGoogle Scholar
  119. National Research Council (2004) Facilitating interdisciplinary research. The National Academies Press, Washington, DC. Accessed 27 Feb 2015Google Scholar
  120. Nelson MP, Vucetich JA (2012) Sustainability science: ethical foundations and emerging challenges. Nat Educ Knowl 3(10):12Google Scholar
  121. Newell P, Frynas JG (2007) Beyond CSR? Business, poverty and social justice: an introduction. Third World Q 28(4):669–681CrossRefGoogle Scholar
  122. Nykvist B (2013) National environmental performance on planetary boundaries: a study for the Swedish Environmental Protection Agency. Swedish Environmental Protection Agency, StockholmGoogle Scholar
  123. OECD (1974) “Core set of indicators”Google Scholar
  124. OECD (1993) OECD core set of indicators for environmental performance reviews: a synthesis report by the Group on the State of the Environment, vol 1(18)(83). OECD, ParisGoogle Scholar
  125. OECD (2002) Indicators to measure decoupling of environmental pressure from economic growth. SG/SD(2002) 1/finalGoogle Scholar
  126. Olteanu A, Castillo C, Diaz F, Kiciman E (2016) Social data: biases, methodological pitfalls, and ethical boundaries. Available at SSRN: https://ssrn.com/abstract=2886526
  127. O’Neil C (2017) Weapons of math destruction: how big data increases inequality and threatens democracy. Broadway Books, New YorkGoogle Scholar
  128. Orts E, Spigonardo J (2014) Sustainability in the age of big data. IGEL/Wharton, University of Pennsylvania, Pennsylvania, p 16Google Scholar
  129. Paddison L (2013) The effectiveness of non-financial reporting—live chat round up. Guardian. https://www.theguardian.com/sustainable-business/effectiveness-non-financial-reporting-live-chat
  130. Parkhurst J (2017) The politics of evidence: from evidence-based policy to the good governance of evidence. Taylor & Francis, RoutledgeGoogle Scholar
  131. Parris TM, Kates RW (2003) Characterizing and measuring sustainable development. Annu Rev Environ Resour 28(1):559–586CrossRefGoogle Scholar
  132. Pauly D (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol Evol 10(10):430CrossRefGoogle Scholar
  133. Pearce W (2014) Scientific data and its limits: rethinking the use of evidence in local climate change policy. Evid Policy J Res Debate Pract 10(2):187–203CrossRefGoogle Scholar
  134. Persson J, Johansson EL, Olsson L (2018a) Harnessing local knowledge for scientific knowledge production: challenges and pitfalls within evidence-based sustainability studies. Ecol Soc 23(4):38CrossRefGoogle Scholar
  135. Persson J, Thorén H, Olsson L (2018b) The interdisciplinary decision problem: Popperian optimism and Kuhnian pessimism in forestry. Ecol Soc 23(3):40CrossRefGoogle Scholar
  136. Petrov O, Gurin J, Manley L (2016) Open data for sustainable development (no. 24017). The World Bank, WashingtonGoogle Scholar
  137. Pintér L, Hardi P, Bartelmus P (2005) Indicators of sustainable development: proposals for a way forward. In: Expert group meeting on indicators of sustainable development, New York, pp 13–15Google Scholar
  138. Poli R (1999) Framing ontology, vol 23, pp 19–26. http://www.formalontology.it/essays/Framing.pdf. Available online from the Ontology resource guide for philosophers
  139. Porter T (1995) Trust in numbers: the pursuit of objectivity in science and public life. Princenton University Press, PrincentonGoogle Scholar
  140. Puschmann C, Burgess J (2014) Big data, big questions metaphors of big data. Int J Commun 8:20Google Scholar
  141. Raupach MR, Davis SJ, Peters GP, Andrew RM, Canadell JG, Ciais P, Friedlingstein P, Jotzo F, Van Vuuren DP, Le Quere C (2014) Sharing a quota on cumulative carbon emissions. Nat Clim Change 4(10):873CrossRefGoogle Scholar
  142. Redman TC (2013) Data’s credibility problem. Harv Bus Rev 91(12):84–88Google Scholar
  143. Rittel HWJ, Webber MM (1972) Dilemmas in a general theory of planning. Policy Sci 4:155–169CrossRefGoogle Scholar
  144. Rockström J, Steffen W, Noone K, Persson Å, Chapin III FS, Lambin E, Lenton TM, Scheffer M, Folke C, Schellnhuber HJ, Nykvist B (2009) Planetary boundaries: exploring the safe operating space for humanity. Ecol Soc 14(2):32. http://www.ecologyandsociety.org/vol14/iss2/art32/(online)
  145. Rockström J, Sachs JD, Öhman MC, Schmidt-Traub G (2013) Sustainable development and planetary boundaries. In: Background research paper submitted to the high level panel on the post-2015 development agenda. Sustainable Development Solutions Network, ParisGoogle Scholar
  146. Ruggie JG (2017) Multinationals as global institution: power, authority and relative autonomy. Regul Govern.  https://doi.org/10.1111/rego.12154 CrossRefGoogle Scholar
  147. Rutter J (2012) Evidence and evaluation in policy making. Institute for Government, London, pp 1–30Google Scholar
  148. Samson A (2014) The behavioral economics guide 2014. Behavioral Science Solutions Ltd., DostupnonaGoogle Scholar
  149. Sandel MJ (2010) Justice: what’s the right thing to do?. Macmillan, New YorkGoogle Scholar
  150. Sarewitz D (2016) The pressure to publish pushes down quality. Nature 533(7602):147CrossRefGoogle Scholar
  151. Scotland J (2012) Exploring the philosophical underpinnings of research: relating ontology and epistemology to the methodology and methods of the scientific, interpretive, and critical research paradigms. English Lang Teach 5(9):9–16.  https://doi.org/10.5539/elt.v5n9p9 CrossRefGoogle Scholar
  152. Searcy C (2016) Measuring enterprise sustainability. Bus Strateg Environ 25(2):120–133CrossRefGoogle Scholar
  153. Shah S, Horne A, Capellá J (2012) Good data won’t guarantee good decisions. Harv Bus Rev 90(4):23–25Google Scholar
  154. Shepherd K, Hubbard D, Fenton N, Claxton K, Luedeling E, Leeuw J (2015) Development goals should enable decision-making. Nature 523(7559):152CrossRefGoogle Scholar
  155. Silberzahn R, Uhlmann EL (2015) Many hands make tight work. Nature 526(7572):189.  https://doi.org/10.1038/526189a CrossRefGoogle Scholar
  156. Slater A, Zwat G (2015) Informing decisions, driving change: the role of data in sustainable future. GRI and Oxfam Novib, AmsterdamGoogle Scholar
  157. Smaldino PE, McElreath R (2016) The natural selection of bad science. R Soc Open Sci 3(9):160384CrossRefGoogle Scholar
  158. Small ML, Sampson RTJ (2014) Executive summary of bringing social science back. In: The ‘big data’ revolution and urban theory. https://www.radcliffe.harvard.edu/executive-summary-bringing-social-science-back-in-%E2%80%98big-data%E2%80%99-revolution-and-urban-theory
  159. Spangenberg JH (2011) Sustainability science: a review, an analysis and some empirical lessons. Environ Conserv 38(3):275–287CrossRefGoogle Scholar
  160. Starr MA (2014) Qualitative and mixed-methods research in economics: surprising growth, promising future. J Econ Surv 28(2):238–264CrossRefGoogle Scholar
  161. Strathern M (1997) ‘Improving ratings’: audit in the British University system. Eur Rev 5(3):305–321CrossRefGoogle Scholar
  162. Sundar N (2000) The construction and destruction of indigenous knowledge in India’s Joint Forest Management Programme. Indigenous environmental knowledge and its transformations. Harwood Academic Publishers, Amsterdam, pp 79–100Google Scholar
  163. Sustainability Context Group (2012) Statement to GRI on the need to enhance treatment of the sustainability context principle in G4, 24 Sep 2012Google Scholar
  164. TEEB (2010) The economics of ecosystems and biodiversity ecological and economic foundations. In: Kumar P (ed) Earthscan, London. http://www.teebweb.org/our-publications/teeb-study-reports/ecological-and-economic-foundations/
  165. Thomke S, Manzi J (2014) The discipline of business experimentation. Harv Bus Rev 92(12):17Google Scholar
  166. Thorén H, Persson J (2013) The philosophy of interdisciplinarity: sustainability science and problem-feeding. J Gen Philos Sci 44(2):337–355CrossRefGoogle Scholar
  167. Trexler M, Schendler A (2015) Science-based carbon targets for the corporate world: the ultimate sustainability commitment, or a costly distraction? J Ind Ecol 19(6):931–933CrossRefGoogle Scholar
  168. Tullock G (2001) A comment on Daniel Klein’s ‘a plea to economists who favor liberty. East Econ J 27(2):203–207Google Scholar
  169. UNDP (2004) Human development report 2004. UNDP, New YorkGoogle Scholar
  170. UNEP (2002) Global environment outlook 3. UNEP, New YorkGoogle Scholar
  171. United Nations (2015) Sustainable development. https://sustainabledevelopment.un.org/?menu=1300. Retrieved from United Nations
  172. Varian HR (2014) Big data: new tricks for econometrics. J Econ Perspect 28(2):3–28CrossRefGoogle Scholar
  173. Wachsmuth D, Cohen DA, Angelo H (2016) Expand the frontiers of urban sustainability. Nat News 536(7617):391CrossRefGoogle Scholar
  174. Ward JS, Barker A (2013) Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821
  175. WB (2004) World development report 2005: a better investment climate for everyone. WB, WashingtonGoogle Scholar
  176. Weller K, Kinder-Kurlanda KE (2015) Uncovering the challenges in collection, sharing and documentation: the hidden data of social media research. In: Standards and practices in large-scale social media research. International Conference on Web and Social Media, OxfordGoogle Scholar
  177. Werner R (2015) The focus on bibliometrics makes papers less useful. Nature 517(7534):245–246CrossRefGoogle Scholar
  178. Wiedmann TO, Schandl H, Lenzen M, Moran D, Suh S, West J, Kanemoto K (2015) The material footprint of nations. Proc Natl Acad Sci 112(20):6271–6276CrossRefGoogle Scholar
  179. Williams M, Zalasiewicz J, Waters CN, Edgeworth M, Bennett C, Barnosky AD, Ellis EC, Ellis MA, Cearreta A, Haff PK, Ivar do Sul JA (2016) The anthropocene: a conspicuous stratigraphical signal of anthropogenic changes in production and consumption across the biosphere. Earth’s Future 4(3):34–53CrossRefGoogle Scholar
  180. World Bank (2015) Open data for sustainable development. World Bank, WashingtonGoogle Scholar
  181. World Bank (2018) Information and communications for development 2018: data-driven development. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/30437. License: CC BY 3.0 IGO
  182. World Council on City Data (2015) About. http://www.dataforcities.org/about. Retrieved from World Council on City Data
  183. WRI (2003) World resources 2002–2004. Decisions for the EarthGoogle Scholar
  184. Xu M, Cai H, Liang S (2015) Big data and industrial ecology. J Ind Ecol 19(2):205–210CrossRefGoogle Scholar
  185. Yarime M (2017) Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities. Sustain Sci 12(6):881–885CrossRefGoogle Scholar
  186. Yarime M (2018) Learning and open data in sustainability transitions: evolutionary implications of the theory of probabilistic functionalism. Environ Syst Decis 38(1):88–91CrossRefGoogle Scholar
  187. Zawistowska S (2015) Big data and corporate sustainability—what is the role of big data in MCNS with corporate sustainability agenda and what is the practical use they can make of it? Masters thesis, Copenhagen Business SchoolGoogle Scholar
  188. Zhang Q, Jiang X, Tong D, Davis SJ, Zhao H, Geng G, Feng T, Zheng B, Lu Z, Streets DG, Ni R (2017) Transboundary health impacts of transported global air pollution and international trade. Nature 543(7647):705CrossRefGoogle Scholar
  189. Zhongming Z, Linong L, Xiaona Y, Wangqiang Z, Wei L (2014) Gap analysis on linking open data for global disaster risk researchGoogle Scholar
  190. Ziemann M, Eren Y, El-Osta A (2016) Gene name errors are widespread in the scientific literature. Genome Biol 17(1):177CrossRefGoogle Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate Program in Sustainability Science, Graduate School of Frontier SciencesThe University of TokyoKashiwaJapan
  2. 2.Division of Public PolicyThe Hong Kong University of Science and TechnologyKowloonChina
  3. 3.Department of Science, Technology, Engineering and Public PolicyUniversity College LondonLondonUK
  4. 4.Graduate School of Public PolicyThe University of TokyoTokyoJapan
  5. 5.Graduate Program in Sustainability Science (GPSS), Graduate School of Frontier SciencesUniversity of TokyoKashiwaJapan

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