**Preface to "Sustainability in the Global-Knowledge Economy"**

The irruption of digital technologies over the last decades has had a profound impact on all economic sectors. The valuation of that impact is still open for discussion, but Corrado, Hulten, and Sichel determined that the share of aggregate intangible capital stock increased three-fold relative to tangible capital during the three decades from 1973 to 2003, reaching a massive 50% of tangible capital, and the figures in developed economies are similar and growing. As the authors note ". . . knowledge capital should be such an important driver of modern economic growth is hardly surprising, given the evidence from everyday life and the results of basic intertemporal economic theory. What is surprising is that intangibles have been ignored for so long and that they continue to be ignored in financial accounting practice at the firm level."(Corrado, Hulten, and Sichel 2009). Digitization, understood as the phenomenon of intelligent connection between products and activities through the exchange of information thanks to digital technologies, allows the modification of business models impacting both suppliers of such goods and services and consumers. As Porter and Heppelmann highlight, this allows companies to exponentially increase the creation of value for their clients through the modification of their value chains thanks to the appearance of new functionalities, greater security in processes and improvements in efficiency, and the optimization of the possibilities offered to their clients (Porter and Heppelmann 2015).

The optimization of these processes involves the management of massive amounts of data that not only help companies but also public administrations, the education sector, and non-governmental organizations to offer more advanced solutions to their customers that enable them to maximize their value creation. However, not all companies are prepared for the optimization of these processes; the adoption of technologies for massive data analysis is not the only factor to be taken into account, as it also influences the way these technologies are adopted and their use. Decisions on these aspects can change the very essence of the development of the companies' activities, even though the end-user hardly finds any differences, beyond the improvement in service efficiency, product quality, and selection improvement (Ballestar et al. 2019).

Robotization and artificial intelligence are the most pervasive dimensions of technological change of the 4.0 industrial revolution. While the process started before WWII, it was not until the 1970s that the auto industry generalized the replacement of manual workers by robots began seriously. During the following decades, it spread across many industries, beginning in integrated production lines but later grew in all sectors, including the services industry, highlighting the large number of occupations that are at peril of being replaced during the next few years, especially after the impact of the COVID-19 pandemic, which will be labor displacing. Artificial intelligence is also gaining ground in a growing range of activities including security, health, and the most basic operation of integrated domestic appliances at home. But this is likely just the beginning, as it will take over most of a mounting number of decision-making processes that for centuries have been the restricted ground of technically proficient experts and professionals, such as lawyers, economists, doctors, bankers, and engineers (Acemoglu and Restrepo 2019; Autor and Salomons 2018; Faia et al. 2020; Frey and Osborne 2017). This will widely affect employment chances at all levels. It will transform aspects like trade union bargaining power, working conditions of a large number of individuals with low skills, or those that do not possess highly specialized technical skills in the STEM area. It will motivate them to redefine their capabilities and knowledge to void the gloomy perspectives of labor instability, low salaries, and social uncertainty.

Unfortunately, labor is not the only risk to sustainability derived from the growing accumulation of knowledge. As Mahnkopf suggests, electronic texts could save millions of trees, reducing greenhouse gas emissions by millions of tons (Mahnkopf 2019). Though there is an increase in the composition of new materials (fiber, siliceous, plastics, etc.), their production and operation require large amounts of energy for the making. Electricity is needed for ICT infrastructure (the cloud, computer warehouses, and data centers that hold digital content using Big Data, e-commerce, the Internet of Things) which is the backbone of the digital economy. Jones estimates that, by 2030, 21% of the global demand for electricity will come from ICT production and use, showing the relevance of new sectors in the future of the economy and change in consumption patterns (Jones 2018).

However, deep learning also has uses in pattern recognition to define the taxonomy of animal and vegetal species, in estimating biodiversity, and in management and conservation, and the automatization of these tasks due to the vast amount of data that would otherwise be lost. Deep learning is also useful in species distribution models, the analysis of carbon cycles, hazard assessment, and prediction, and forest management thanks to the need for vast amounts of data for classification, modeling, and prediction in forest ecology research. As there is an increase in data recollection and new models adapted to its use, all branches of ecology will take advantage of the new methodologies to gain efficiency, using newly design algorithms to spur knowledge and research on all earth cycle issues (Christin, Hervet, and Lecomte 2019; Liu et al. 2018; Reichstein et al. 2019). But, as Huntingford et al. propose, machine learning models will probably be more useful for the analysis of climate change, as they will be able to deliver ". . . new insights into the incredibly rich diversity of interconnected Earth System behaviours and their multiple interactions with biochemical cycles. . . [revealing] . . . climate system attributes and enhance forecasting across time scales, it is AI that can then adopt this information to support decisions." (Huntingford et al. 2019).

While former issues are usually downplayed, or just ignored, the latter are somehow exaggerated when speaking about the impact of the knowledge economy on sustainability. The following pages will detail its positive impacts in areas like logistics, agriculture, etc., leaving space for more perspectives on the process, which is arguably one of the most relevant in the history of humankind and should help overcome some of the issues related to environmental decay and climate change.

#### **References**

Acemoglu, Daron, and Pascual Restrepo. 2019. "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand." IZA Discussion Paper (12292). Available online: http://www.nber. org/papers/w25682.pdf (accessed on 5 January 2020).

Autor, David, and Anna Salomons. 2018. Brookings Papers on Economic Activity Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share. Available online: https://www.brookings.edu/bpea-articles/is-automation- labor-displacingproductivity-growth-employment-and-the-labor-share/ (accessed on 30 December 2019).

Ballestar, Mar´ıa Teresa, Luis Miguel Doncel, Jorge Sainz, and Arturo Ortigosa-Blanch. 2019. A Novel Machine Learning Approach for Evaluation of Public Policies: An Application in Relation to the Performance of University Researchers. *Technological Forecasting and Social Change* 149.

Christin, Sylvain, Eric Hervet, and Nicolas Lecomte. 2019. Applications for Deep Learning in ´ Ecology. *Methods in Ecology and Evolution* 10: 1632–44. doi:10.1111/2041-210X.13256.

Corrado, Carol, Charles Hulten, and Daniel Sichel. 2009. Intangible Capital and U.S. Economic Growth. *Review of Income and Wealth* 55: 661–85.

Faia, Ester, Sebastien Laffitte, Maximilian Mayer, and Gianmarco Ottaviano. 2020. IZA working ´ papers Automation , Globalization and Vanishing Jobs: A Labor Market Sorting View.

Frey, Carl Benedikt, and Michael A. Osborne. 2017. The Future of Employment: How Susceptible Are Jobs to Computerisation? *Technological Forecasting and Social Change* 114: 254–80.

Huntingford, Chris et al. 2019. Machine Learning and Artificial Intelligence to Aid Climate Change Research and Preparedness. *Environmental Research Letters* 14.

Jones, Nicola. 2018. How to Stop Data Centres from Gobbling up the World's Electricity. *Nature* 561: 163–66.

Liu, Zelin et al. 2018. Application of Machine-Learning Methods in Forest Ecology: Recent Progress and Future Challenges. *Environmental Reviews* 26: 339–50. doi:10.1139/er-2018-0034.

Mahnkopf, Birgit. 2019. EuroMemo Group Discussion Paper No. 01/2019 The '4 Th Wave of Industrial Revolution'—A Promise Blind to Social Consequences, Power and Ecological Impact in the Era of 'Digital Capitalism'.

Porter, Michael E., and James E. Heppelmann. 2015. How Smart, Connected Products Are Transforming Companies. *Harvard Business Review* 2015.

Reichstein, Markus et al. 2019. Deep Learning and Process Understanding for Data-Driven Earth System Science. *Nature* 566: 195–204. doi:10.1038/s41586-019-0912-1.

> **Joan Torrent-Sellens, Jorge Sainz-Gonz´alez** *Editors*

*Article*
