A circularity accounting network: CO2 measurement along supply chains using machine learning

  1. Jesse, Forrest Fabian 1
  2. Antonini, Carla 2
  3. Luque-Vilchez, Mercedes 3
  1. 1 University of Washington / Beijing Xixuan Laboratory
  2. 2 Universidad Autónoma de Madrid

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

  3. 3 Universidad de Córdoba

    Universidad de Córdoba

    Córdoba, España

    ROR https://ror.org/05yc77b46

Revista de contabilidad = Spanish accounting review: [RC-SAR]

ISSN: 1138-4891

Year of publication: 2023

Volume: 26

Issue: 0

Pages: 21-33

Type: Article

DOI: 10.6018/RCSAR.564901 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de contabilidad = Spanish accounting review: [RC-SAR]


This paper proposes to use a type of machine learning network called artificial neural networks to design a circularity accounting network. The network is composed of human and non-human actors and accounts for the impact of products’ CO2 emissions and sequestration along global supply chains.  The network serves to connect people and other actors that share a CO2 indicator and allows users to visualize the level of (un-) circularity of different products through specific diagrams calculated by a CO2 estimator drawing on insights from actor-network theory. Unlike most previous circular economy accounting studies that develop some type of framework or indicator that represent measurements at micro, meso or macro levels, the circularity accounting network is not confined to a particular level of analysis but is designed to build relationships between multiple users at different levels (e.g., government, corporate or consumer actors). The paper presents the conceptual design and a preliminary test of the network using real data, helping to advance the underexplored potential of artificial intelligence in the field of circular economy accounting. The main contribution of this network is that data provided by the indicator: (i) is derived from the network itself learning from open sources, the network (ii) is not static but keeps flowing as new relationships are built within the network, moving toward self-regulating, (iii) contemplates both emissions and sequestrations along supply chains.

Bibliographic References

  • Aranda-Usón, A., M. Moneva, J., Portillo-Tarragona, P., & Llena-Macarulla, F. (2018). Measurement of the circular economy in businesses: Impact and implications for regional policies. Economics and Policy of Energy and the Environment, 2(1),187–205. https://doi.org/10.3280/EFE2018-002010
  • Aranda-Usón, A., Portillo-Tarragona, P., Marín-Vinuesa, L.M., & Scarpellini, S. (2019). Financial Resources for the Circular Economy: A Perspective from Businesses. Sustainability, 11(3), 1–23. https://doi.org/10.3390/su11030888
  • Aranda-Usón, A., Portillo-Tarragona, P., Scarpellini, S.. & Llena-Macarulla, F. (2020). The progressive adoption of a circular economy by businesses for cleaner production: An approach from a regional study in Spain. Journal of Cleaner Production, 247, 119648. https://doi.org/10.1016/j.jclepro.2019.119648
  • Aranda-Usón, A., Moneva, J.M., Scarpellini, S. (2022). ‘Circular sustainability accounting’ in businesses for a circular economy: a framework of analysis. European Journal of Social Impact and Circular Economy. https://doi.org/10.13135/2704-9906/6817
  • Ashmore, M., Wooffitt, R. and Harding, S. (1994). Humans and others, agents and things Humans and Others: The Concept of Agency and its Attribution [special issue] American Behavioural Scientist, 37(6), pp. 733–741.
  • Bakan, J. (2004). The Corporation: The Pathological Pursuit of Profit and Power. London, UK: Ed. Constable.
  • Barter, N., & Bebbington, J. (2013). Actor-network theory: a briefing note and possibilities for social and environmental accounting research. Social and Environmental Accountability Journal, 33(1), 33-50. https://doi.org/10.1080/0969160X.2012.743264
  • Bebbington, J., Österblom, H., Crona, B., Jouffray, J.-B., Larrinaga, C., Russell, S., & Scholtens, B. (2019). Accounting and accountability in the Anthropocene. Accounting, Auditing and Accountability Journal, 33(1), 152-177. https://doi.org/10.1108/AAAJ-11-2018-3745
  • Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015). Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. Advances in Neural Information Processing Systems. 28(1), 1171-1179. https://proceedings.neurips.cc/paper/2015/file/e995f98d56967d946471af29d7bf99f1-Paper.pdf
  • Bentué, D. B., Fondevila, M. M., & Scarpellini, S. (2022). Financial institutions facing the challenge of the European taxonomy of sustainable investments and the circular economy disclosure. UCJC Business & Society Review, 19(2), 120-161.
  • Berkes, F., & Ross, H. (2013). Community resilience: toward an integrated approach. Society & Natural Resources, 26(1), 5-20. https://doi.org/10.1080/08941920.2012.736605
  • Briers, M., & Chua, W. F. (2001). The role of actor-networks and boundary objects in management accounting change: a field study of an implementation of activity based costing, Accounting, Organisations and Society, 26(1), pp. 237–269. https://doi.org/10.1016/S0361-3682(00)00029-5
  • Britz, D., Goldie, A., Luong, M.-T., & Le, Q. (2017). Massive Exploration of Neural Machine Translation Architectures [Paper presentation]. EMNLP 2017 Conference on Empirical Methods in Natural Language Processing, Proceedings, (pp. 1442–1451). https://arxiv.org/abs/1703.03906v2
  • Callon, M. (1986). Some elements in a sociology of translation: domestication of the scallops and fishermen of St Brieuc Bay. In J. Law (Ed.), Power, Action and Belief: A New Sociology of Knowledge?, pp. 196–233. London, UK: Routledge.
  • Castree, N. (2002). False antitheses? Marxism, nature and actor-networks, Antipode, 34(1), pp. 111–116.
  • CCaLC project (2021). Carbon Calculations over the Life Cycle of Industrial Activities. http://www.ccalc.org.uk/casestudies.php
  • CDP (Carbon Disclosure Project), 2011. Carbon disclosure project 2011 Deutschland/ Österreich 250. https://www.cdp.net/en/guidance/guidance-for-companies (accessed 03.07.23)
  • Chan, V., & Chan, C. (2017). Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm. Petroleum, 3(1), 56-67. https://doi.org/10.1016/j.petlm.2016.11.004
  • Chen, M., Liu, Q., Huang, S., & Dang, C. (2020). Environmental cost control system of manufacturing enterprises using artificial intelligence based on value chain of circular economy. Enterprise Information Systems, 1-20. https://doi.org/10.1080/17517575.2020.1856422
  • Cheshmberah, F., Fathizad, H., Parad, G. A., & Shojaeifar, S. (2020). Comparison of RBF and MLP neural network performance and regression analysis to estimate carbon sequestration. International Journal of Environmental Science and Technology, 17(9), 3891-3900. https://doi.org/10.1007/s13762-020-02696-y
  • Costanza, R. (1980). Embodied energy and economic valuation. Science, 210(4475), 1219-1224.
  • de-Magistris, T., Gracia, A., & Barreiro-Hurle, J. (2017). Do consumers care about European food labels? An empirical evaluation using best-worst method. British Food Journal, 119(12), 2698-2711. https://doi.org/10.1108/BFJ-11-2016-0562
  • Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896. https://doi.org/10.1108/TQM-10-2019- 0243
  • Ellen MacArthur Foundation (2015a). Towards a Circular Economy Economic and Business Rationale for an Accelerated Transition. https://ellenmacarthurfoundation.org/towards-the-circular-economy-vol-1-an-economic-and-business-rationale-for-an
  • Ellen MacArthur Foundation (2015b). Delivering the Circular Economy: A Toolkit for Policymakers, Delivering the Circular Economy: A Toolkit for Policymakers. European Union. https://circulareconomy.europa.eu/platform/en/toolkits-guidelines/delivering-circular-economy-toolkit-policymakers
  • European Commission (2014). Towards a circular economy: a zero waste programme for Europe (bl 398). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions.
  • European Commission (2015). Closing the Loop An EU Action Plan for the Circular Economy. Communication From the Commission to the European Parliament. The Council, the European Economic and Social Committee and the Committee of the Regions.
  • European Commission (2019). Communication from the Commission to the European Parliament, the European Council, the European Economic and Social Committee and the Committee of the regions. The European Green Deal. Brussels, 11.12.2019 COM (2019) 640 final.
  • European Commission (2020). The New Circular Economy Action Plan.
  • Fox, S. (2000). Communities of practice, Foucault and actor-network theory, Journal of Management Studies, 37(6), pp. 853–867.
  • Ghisellini, P., Cialani, C., & Ulgiati, S. (2016). A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems. Journal of Cleaner Production, 114(1), 11–32. https://doi.org/10.1016/j.jclepro.2015.09.007
  • Gladwin, T. N., Kennelly, J. J., & Krause, T. S. (1995). Shifting paradigms for sustainable development: implications for management theory and research. Academy of Management Review, 20(4), pp. 874–907. https://doi.org/10.2307/258959
  • Gray, R. (1992). Accounting and environmentalism: An exploration of the challenge of gently accounting for accountability, transparency and sustainability. Accounting, Organizations and Society, 17(5), 399–425. https://doi.org/10.1016/0361-3682(92)90038-T
  • Gray, R., Bebbington, J., & Walters, D. (1993). Accounting for the Environment (London: Paul Chapman) Griffin, P., & Heede, C. R. (2017). The carbon majors database. CDP carbon majors report 2017, Carbon Disclosure Project (CDP) UK.
  • GRI (Global Reporting Initiative) (2011). 2011 G3.1. https://www.globalreporting.org/standards/ (accessed 03.07.23)
  • Grunert, K., Hieke, S., & Wills, J. (2014). Sustainability labels on food products: Consumer motivation, understanding and use. Food Policy, 44(1), 177-189. https://doi.org/10.1016/j.foodpol.2013.12.001
  • Heede, R. (2019). Carbon Majors: Accounting for carbon and methane emissions 1854-2010 Methods & Results Report. London, UK: LAP Lambert Academic Publishing.
  • Ibáñez-Forés, V., Martínez-Sánchez, V., Valls-Val, K., & Bovea, M. D. (2022). Sustainability reports as a tool for measuring and monitoring the transition towards the circular economy of organisations: Proposal of indicators and metrics. Journal of Environmental Management, 320. https://doi.org/10.1016/j.jenvman.2022.115784
  • Ivakhiv, A. (2002) Toward a multicultural ecology, Organization and Environment, 15(4), 389–409. http://www.jstor.org/stable/26161759
  • Jevons, W.S. (1865). The Coal Question (2nd ed.). New York, USA: Macmillan and Company.
  • Jin, L., Tan, F., & Jiang, S. (2020). Generative Adversarial Network Technologies and Applications in Computer Vision. Computational Intelligence and Neuroscience, 1-17. https://doi.org/10.1155/2020/1459107
  • Jin, H. (2021). Prediction of direct carbon emissions of Chinese provinces using artificial neural networks. PLOS One, 16(5), e0236685. https://doi.org/10.1371/journal.pone.0236685
  • Jose, R., Panigrahi, S. K., Patil, R. A., Fernando, Y., & Ramakrishna, S. (2020). Artificial Intelligence-Driven Circular Economy as a Key Enabler for Sustainable Energy Management. Materials Circular Economy, 2(1), 1-7. https://doi.org/10.1007/s42824-020-00009-9
  • Karnow, C. E.A. (1996). Liability for Distributed Artificial Intelligences. Berkeley Technology Law Journal, 11(1), 147. https://www.jstor.org/stable/24115584
  • Katz Gerro, T., & López Sintas, J. (2019). Mapping circular economy activities in the European Union: Patterns of implementation and their correlates in small and medium‐sized enterprises. Business Strategy and the Environment, 28(1), 485–496. https://doi.org/10.1002/bse.2259
  • Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: an analysis of 114 definitions. Resources, Conservation and Recycling, 127 (1), 221-232. https://doi.org/10.1016/j.resconrec.2017.09.005
  • Koperna, G., Jonsson, H., Ness, R., Cyphers, S., & MacGregor, J. (2020). A Workflow Incorporating an Artificial Neural Network to Predict Subsurface Porosity for CO2 Storage Geological Site Characterization. Processes, 8(7), 813. https://doi.org/10.3390/pr8070813
  • Korhonen, J., Honkasalo, A., & Seppälä, J. (2018). Circular economy: the concept and its limitations. Ecological Economics, 143(1), 37–46. https://doi.org/10.1016/j.ecolecon.2017.06.041
  • Lade, S. J., Steffen, W., de Vries, W., Carpenter, S. R., Donges, J. F., Gerten, D., ... & Rockström, J. (2020). Human impacts on planetary boundaries amplified by Earth system interactions. Nature Sustainability, 3(2), 119–128. https://doi.org/10.1038/s41893-019-0454-4.
  • Latour, B. (1993). We Have Never Been Modern, trans. Catherine. Cambridge, MA, USA: Harvard University Press.
  • Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network Theory. Oxford, UK: Oxford University Press.
  • Latour, B. (2010). On the Modern Cult of the Factish Gods. London, UK: Duke University Press.
  • Latour, B. (2017). Facing Gaia: Eight lectures on the new climatic regime. New York, USA: John Wiley & Sons.
  • Law, J. (1992). Notes on the Theory of the Actor Network: Ordering, Strategy and Heterogeneity (Centre for Science Studies, Lancaster University). Available at: http://www.comp.lancs.ac.uk/sociology/papers/Law-Notes-on-ANT.pdf (accessed 20 June 2008)
  • Law, J. (1999). After ANT: complexity, naming and topology. In J. Law and J. Hassard (Eds), Actor Network Theory and After, pp. 1–14. Oxford, UK: Blackwell.
  • Law, J. (2000). Objects, Spaces and Others (Centre for Science Studies, Lancaster University). Available at: http://www. comp.lancs.ac.uk/sociology/papers/Law-Objects-Spaces-Others.pdf (accessed 20 June 2008).
  • Lee, N., & Brown, S. (1994). Otherness and the actor network: the undiscovered continent, Humans and Others: The Concept of Agency and its Attribution [special issue]. American Behavioural Scientist, 37(6), pp. 772–791.
  • Lee, N., & Hassard, J. (1999). Organization unbound: actor network theory, research strategy and institutional flexibility, Organization, 6(3), pp. 391–404. https://doi.org/10.1177/135050849963002
  • Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Cham: Springer. https://doi.org/10.1007/978-3-319-10602-1_48.
  • Linder, M., Sarasini, S., & van Loon, P. (2017). A metric for quantifying product-level circularity. Journal of Industrial Ecology, 21, 545e558. https://doi.org/10.1111/jiec.12552
  • Liu, Q., Trevisan, A. H., Yang, M., & Mascarenhas, J. (2022). A framework of digital technologies for the circular economy: Digital functions and mechanisms. Business Strategy and the Environment, 31(5), 2171-2192. https://doi.org/10.1002/bse.3015
  • Llena-Macarulla, F., Moneva, J. M., Aranda-Usón, A., & Scarpellini, S. (2023). Reporting measurements or measuring for reporting? Internal measurement of the Circular Economy from an environmental accounting approach and its relationship. Revista de Contabilidad-Spanish Accounting Review, 26(2), 200-212. https://doi.org/10.6018/rcsar.467751
  • Lowe, A. (2001). After ANT: an illustrative discussion of the implications for qualitative accounting case research. Accounting, Auditing and Accountability, 14(3), pp. 327–351. https://doi.org/10.1108/EUM0000000005519
  • Lukka, K., & Vinnari, E. (2014). Domain theory and method theory in management accounting research. Accounting, Auditing & Accountability Journal, 27(8), 1308-1338. https://doi.org/10.1108/AAAJ-03-2013-1265
  • Marco-Fondevila, M., Llena-Macarulla, F., Callao-Gastón, S., & Jarne-Jarne, J.I. (2021). Are circular economy policies actually reaching organizations? Evidence from the largest Spanish companies. Journal of Cleaner Production, 285, 124858. https://doi.org/10.1016/j.jclepro.2020.124858
  • Moneva, J. M., Archel, P., & Correa, C. (2006). GRI and the camouflaging of corporate unsustainability. Accounting Forum, 30(2), 121–137. https://doi.org/10.1016/j.accfor.2006.02.001
  • Moneva, J. M., Scarpellini, S., Aranda‐Usón, A., & Alvarez Etxeberria, I. (2023). Sustainability reporting in view of the European sustainable finance taxonomy: Is the financial sector ready to disclose circular economy?. Corporate Social Responsibility and Environmental Management, 30(3), 1336-1347. https://doi.org/10.1002/csr.2423
  • Murray, A., Skene, K., & Haynes, K. (2017). The circular economy: an interdisciplinary exploration of the concept and application in a global context. Journal of Business Ethics, 140(3), 369– 380. https://doi.org/10.1007/s10551-015-2693-2
  • Nikseresht, A., Hajipour, B., Pishva, N., & Mohammadi, H. A. (2022). Using artificial intelligence to make sustainable development decisions considering VUCA: a systematic literature review and bibliometric analysis. Environmental Science and Pollution Research, 1-30. https://doi.org/10.1007/s11356-022-19863-y
  • O’Connell, D., Ciccotosto, S. K., & De Lange, P. A. (2009). Latour’s contribution to the accounting literature through actor-network theory: a critical appraisal, paper presented at Interdisciplinary Perspectives on Accounting Conference.
  • Ogunmakinde, O. E. (2019). A review of circular economy development models in China, Germany and Japan. Recycling, 4(3), 27. https://doi.org/10.3390/recycling4030027
  • Pauliuk, S. (2018). Critical appraisal of the circular economy standard bs 8001:2017 and a dashboard of quantitative system indicators for its implementation in organisations. Resources Conservation and Recycling, 129(1), 81– 92. https://doi.org/10.1016/j.resconrec.2017.10.019
  • Rahimi, M., Moosavi, S. M., Smit, B., & Hatton, T. A. (2021). Toward smart carbon capture with machine learning. Cell Reports Physical Science. 2, 100396. https://doi.org/10.1016/j.xcrp.2021.100396
  • Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., de Wit, C. A., Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U.,… & Foley, J. (2009). A safe operating space for humanity. Nature, 461(7263), 472-475. https://doi.org/10.1038/461472a
  • Rodrigue, M., & Romi, A. M. (2022). Environmental escalations to social inequities: Some reflections on the tumultuous state of Gaia. Critical Perspectives on Accounting, 82, 102321. https://doi.org/10.1016/j.cpa.2021.102321
  • Rossi, E., Bertassini, A.C., Ferreira, C. dos S., Neves do Amaral, W.A., & Ometto, A.R. (2020). Circular economy indicators for organizations considering sustainability and business models: Plastic, textile and electro-electronic cases. Journal of Cleaner Production, 247(1). https://doi.org/10.1016/j.jclepro.2019.119137
  • Sassanelli, C., Rosa, P., Rocca, R., & Terzi, S. (2019). Circular economy performance assessment methods: A systematic literature review. Journal of Cleaner Production, 229(1), 440-453. https://doi.org/10.1016/j.jclepro.2019.05.019
  • Scarpellini, S., Marín-Vinuesa, L. M., Aranda-Usón, A., & Portillo-Tarragona, P. (2020). Dynamic capabilities and environmental accounting for the circular economy in businesses. Sustainability Accounting, Management and Policy Journal, 11(7), 1129-1158. https://doi.org/10.1108/SAMPJ-04-2019-0150
  • Scarpellini, S. (2022). Social impacts of a circular business model: An approach from a sustainability accounting and reporting perspective. Corporate Social Responsibility and Environmental Management, 29(3), 646-656. https://doi.org/10.1002/csr.2226
  • Schaltegger, S., & Csutora, M. (2012). Carbon accounting for sustainability and management. Status quo and challenges. Journal of Cleaner Production, 36(1), 1–16. https://doi.org/10.1016/j.jclepro.2012.06.024
  • Schröder, P., Bengtsson, M., Cohen, M., Dewick, P., Hofstetter, J., & Sarkis, J. (2019). Degrowth within –aligning circular economy and strong sustainability narratives. Resources Conservation and Recycling, 146(1), 190–191. https://doi.org/10.1016/j.resconrec.2019.03.038
  • Schulze, G. (2016). Growth Within: A Circular Economy Vision for a Competitive Europe. Ellen MacArthur Foundation, Deutsche Post Foundation and McKinsey Center for Business and Environment. https://unfccc.int/sites/default/files/resource/Circular%20economy%203.pdf
  • Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017). A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia computer science, 114(1), 473-480. https://doi.org/10.1016/j.procs.2017.09.031
  • Sipöcz, N., Tobiesen, F. A., & Assadi, M. (2011). The use of Artificial Neural Network models for CO2 capture plants. Applied Energy, 88(7), 2368-2376. https://doi.org/10.1016/j.apenergy.2011.01.013
  • SmartLabel. (2021, October 9). Kellogg's® Frosted Flakes® cereal. Retrieved October 18, 2021, from https://smartlabel.kelloggs.com/Product/Index/00038000199042
  • Song, Y., Wonmo S., Youngho J., & Woodong J. (2020). Application of an Artificial Neural Network in Predicting the Effectiveness of Trapping Mechanisms on CO2 Sequestration in Saline Aquifers. International Journal of Greenhouse Gas Control, 98, 103042. https://doi.org/10.1016/j.ijggc.2020.103042
  • Sutskever, I., Vinyals, O., & Quoc, V. L. (2014). Sequence to Sequence Learning with Neural Networks. In NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems Volume 2 December 2014 (pp. 3104–3112).
  • Swensson, N., & Funck, E.K. (2019). Management control in a circular economy. Exploring and theorizing the adaptation of management control to circular business models. Journal of Cleaner Production, 233(1), 390–398. https://doi.org/10.1016/j.jclepro.2019.06.089
  • Szilárd, L. (1929). Über die Entropieverminderung in einem thermodynamischen System bei Eingriffen intelligenter Wesen. Zeitschrift für Physik, 53(11), 840-856.
  • Thanh, H. V., Sugai, Y., & Sasaki, K. (2020). Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones. Scientific reports, 10(1), 1-16. https://doi.org/10.1038/s41598-020-73931-2
  • Thomassen, M. A., van Calker, K. J., Smits, M. C., Iepema, G. L., & de Boer, I. J. (2008). Life cycle assessment of conventional and organic milk production in the Netherlands. Agricultural systems, 96(1-3), 95-107. https://doi.org/10.1016/j.agsy.2007.06.001
  • Walls, J.L., & Paquin, R.L. (2015). Organizational perspectives of industrial symbiosis: a review and synthesis. Organization & Environment, 28, 32e53. https://doi.org/10.1177/ 1086026615575333.
  • Wilson, M., Paschen, J., & Pitt, L. (2021). The Circular Economy Meets Artificial Intelligence (AI): Understanding the Opportunities of AI for Reverse Logistics. Management of Environmental Quality: An International Journal, 114(1), 473–480. doi:10.1108/MEQ-10-2020-0222.
  • Wishart, L., & Antheaume, N. (2021). Accounting for circularity. In J. Bebbington, C. Larrinaga, B. O’Dwyer, & I. Thomson (Eds), Handbook of Environmental Accounting (pp. 251-262). London, UK: Routledge. https://doi.org/10.4324/9780367152369-21
  • York, A. M., Otten, C. D., BurnSilver, S., Neuberg, S. L., & Anderies, J. M. (2021). Integrating institutional approaches and decision science to address climate change: a multi-level collective action research agenda. Current Opinion in Environmental Sustainability, 52, 19-26. https://doi.org/10.1016/j.cosust.2021.06.001
  • Zeng, H., Chen, X., Xiao, X., & Zhou, Z. (2017). Institutional pressures, sustainable supply chain management, and circular economy capability: Empirical evidence from Chinese eco-industrial park firms. Journal of Cleaner Production, 155, 54-65. https://doi.org/10.1016/j.jclepro.2016.10.093
  • Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2019). Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). Available at https://arxiv.org/pdf/1805.08318.pdf
  • Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67(1), 126-139. https://doi.org/10.1016/j.eswa.2016.09.027