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The need to rapidly decarbonise economies raises questions about whether countries' workforces possess the requisite skills to achieve the net zero transition as well as the capacity to redeploy workers from 'brown' to 'green' jobs. This paper applies a task-based framework to granular data from the Occupational Information Network (O*NET) and country-specific employment sources to generate new indicators of the green skills structure of labour markets for a large number of OECD countries and non-OECD EU countries.

Significant cross-country differences emerge in the underlying supply of green skills and the potential of economies to reallocate brown job workers to green jobs within their broad occupation categories. In a majority of detailed brown occupations, workers have in principle the necessary skills to transition to green jobs, with the exception of those in production occupations, who may require more extensive re-skilling. In contrast, workers from most highly automatable occupations are generally not found to have the sufficient skills to transition to green jobs, suggesting more limited scope for the net-zero transition to reinstate labour displaced by automation.

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This paper is part of a study on the impact of automation on training. The study concentrates on the skills required of middle-level technical workers. Show less

Three main policy responses to the labour market challenges posed by robotisation and automation have emerged in the research literature. The first is 'taxing robots' and using this revenue to introduce a basic income that could offset the negative impacts of replacing humans by robots. The second option highlights the ownership of robots so that taking part in the new source of wealth is possible. The third focuses on strengthening the comparative advantages, the creativity, and the social intelligence of humans that robots will never be able to match. All of these policy responses are supported by economic rationales and research findings, but a systematic review shows that all of them raise further questions and challenges that should be carefully investigated in order to choose the right path. This paper offers a comprehensive overview of these questions. Furthermore, in a broader sense these policy options - redistributing the benefits of technological changes, increasing accesses to the benefits and utilisation of changes, and supporting the individual and institutional adjustment to changes - are relevant to every technological transformation. Hence, the lessons that are drawn from the current discussion of policy options driven by specific technologies, robotization, and automation might serve as a precursor to potential policy responses triggered by other technologies.

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Powerful new automation technologies such as machine learning, artificial intelligence (AI), and advanced robotics are already transforming the Australian economy and the workplace. These technologies present an enormous opportunity to restore momentum to the Australian economy and extend the nation's 30-year economic boom in an inclusive way. However, the potential scale and distributional effects of this disruption need to be carefully managed. This report details how automation and AI will affect Australia. Automation and AI will be disruptive, just as other technology adoptions have been disruptive in the past. They will displace jobs and disrupt workplaces and some vulnerable communities. However, the extent of this change is neither unprecedented nor unmanageable, and the benefits will be lasting. If Australia embraces automation with an eye to national interests, the nation will be able to develop new business opportunities, boost productivity, and create better-paying jobs - keeping employment high and reinvigorating per capita income growth sufficiently to offset the impact of an aging population. As we adopt automation and AI, there will certainly be challenges to navigate, but Australia has in the past confronted similar challenges head-on and found ways to maximize opportunities to build sustainable national and income growth for all.

This report sets out why and how Australia must push for the win–win scenario of inclusive growth by pursuing actions that both accelerate automation and adoption and share its benefits. It's a vision well worth focusing on. With foresight, proactive leadership from the government, business, and education sectors, and a commitment to act, Australia can capture the opportunity offered by automation, manage the transition, and ensure the gains are broadly shared.

Key findings of the report include the following: (1) An estimated 44 per cent of existing workforce activities in Australia could be automated with existing technologies, and this automation potential could rise to 63 per cent by 2030; (2) An estimated 25 per cent of automation potential is expected to be adopted in practice by 2030, with an estimated 3.5 million to 6.5 million full-time jobs displaced. However, we also estimate 2.8 million to 4.2 million new jobs could be created by 2030 from increased productivity driven by automation and economic growth. This opportunity could add $1.1 trillion to $4.0 trillion to the economy over the next 15 years, providing every Australian with $4,000 to $11,000 in additional income per year by 2030; (3) While some jobs will be lost and others created, all jobs will change. As automation technologies integrate into the workforce, the mix of skills required in all jobs will shift. Up to 35 per cent of Australians will need to change occupations or upgrade their skills by 2030 and 1.8 million to 5.0 million workers will need to change professions; (4) Without proactive leadership to manage the transition to the new steady state, automation will have widespread, large-scale, and disruptive distribution impacts. During the peak of the transition, increased job churn could see Australia's unemployment rate temporarily spike by up to 2.5 per cent without proactive policies to manage the transition. Without retraining for vulnerable workers and those in vulnerable regions, income inequality could widen by up to 30 per cent; and (5) Policymakers will need to respond to a range of implications, including rising unemployment and income inequality, growing disparities between cities and rural communities, shifts in the global competitiveness of our export-oriented sectors, and reskilling and redeploying a large number of displaced workers.

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Workforce planning is the strategic centrepiece for Jobs and Skills Councils to inform and establish each of their other functions. The workforce plan serves as a guide to identifying the contemporary drivers and challenges within Australia's rail industry and developing forward-thinking actions to address those challenges.

The workforce plan includes an industry overview, megatrends, key challenges and drivers, proposed actions, proposed action schedule, future research and consultation, approach to consultation, and existing workforce strategies and initiatives. Appendix A includes the reference data and charts and Appendix B is a glossary of terms.

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We examine the differential effects of automation on the labor market and educational outcomes of women relative to men over the past four decades. Although women were disproportionately employed in occupations with a high risk of automation in 1980, they were more likely to shift to high-skill, high-wage occupations than men in over time. We provide a causal link by exploiting variation in local labor market exposure to automation attributable to historical differences in local industry structure. For a given change in the exposure to automation across commuting zones, women were more likely than men to shift out of routine task-intensive occupations to high-skill, high wage occupations over the subsequent decade. The net effect is that initially routine-intensive local labor markets experienced greater occupational gender integration. College attainment among younger workers, particularly women, also rose significantly more in areas more exposed to automation. We propose a model of occupational choice with endogenous skill investments, where social skills and routine tasks are q-complements, and women have a comparative advantage in social skills, to explain the observed patterns. Supporting the model mechanisms, areas with greater exposure to automation experienced a greater movement of women into occupations with high social skill (and high cognitive) requirements than men.

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In this white paper, The Adecco Group draws upon internal insights and international expertise to illustrate the crucial role that skills investment needs to play in moving toward a Green Economy. Skills investment and development need to be understood not merely as a result of the transition, but rather as the decisive factor that enables the Green Transition in the first place. Highlighting the policy context for the Green Transition of labour markets and business worldwide, we discuss megatrends in the World of Work and the necessary components skills policies need to address in order to best achieve a fair and equitable transition process.

Based on the challenges faced by two key sectors undergoing drastic changes to their established business models and the skills they require - the energy sector and the automotive industry - we illustrate how embracing truly sustainable workforce solutions can lead the way towards a greener future. We make the case that only an inclusive and collaborative approach involving all labour market stakeholders can ensure that skills requirements for the Green Economy are met and that workers are not left behind. The observations in this paper are completed by a set of key actions that we recommend labour market stakeholders should take now. In order to secure the skills landscape needed for the Green Economy, we encourage all labour market stakeholders to realise their common expectations and responsibilities within a New Social Contract.

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This document serves both as a conceptual and practical guide for defining and classifying [artificial intelligence] AI, in order to help stakeholders analyse and understand its impact on the workplace. It first discusses how AI can be defined and provides a selection of AI use cases to help stakeholders identify AI and distinguish it from other advanced technologies. The document then provides a framework for classifying AI according to its impact on the workplace, consisting of a set of questions intended to help stakeholders evaluate any AI application from a workplace perspective (either a priori or ex post) and to promote informed discussion so that AI is implemented in a way that empowers and complements workers and improves job quality, and that no one is left behind.

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This study addresses the dearth of gender analysis within debates about technological innovation and workplace change. Qualitative analysis of 12 focus groups conducted with women in 'frontline' and 'professional' roles discussing their use and engagement with digital technologies at work reveals contrasting narratives of 'digital frustration' and 'digital invigoration'. To explain these distinct narratives, we synthesise insights from science and technology studies with findings from scholarship on gendered work and labour market inequality to show that these differences are not driven solely by a technology's form or the degree of automation it ostensibly represents. Instead, women's narratives reflect an interplay between technological design, employment context and workers' own voice and agency. These findings challenge assumptions about the totalising and transformative power of work-related technologies, redirecting attention to how social and political contestations over digital technologies inform worker experiences and shape the future of work.

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Funded by the EU's Horizon 2020 programme, Beyond 4.0 examines the impact of new digital technologies on the future of jobs, business models and welfare in the European Union (EU). This policy briefing discusses how the policy push for a new Industry 5.0 can be realised. It first outlines Industry 4.0 and its limitations. It then describes Industry 5.0 and, learning the lessons from the experience of Industry 4.0, outlines what is required to make Industry 5.0 happen. In respect of this last aim, the briefing makes a number of recommendations about Industry 5.0 as concept, policy and practice: (1) provide conceptual refinement and develop Industry 5.0 indicators and measurement; (2) plug the data deficit to enable evaluation of Industry 5.0; (3) develop a business case to help persuade companies of the benefits of Industry 5.0; (4) monitor progress on the adoption of Industry 5.0 and provide support when needed; and (5) ensure that Industry 5.0 is integrated into other social, economic and political EU policy.

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