Technology Adoption and Sustainable Development: Evaluating the Impact of Robotics on Unemployment and SO₂ Emissions

Abstract

This doctoral thesis explores the impact of robotics adoption on two dimensions of sustainability, unemployment and SO₂ emissions. The adoption of robotics has brought about profound changes in the global community, transforming employment and the air quality. Two seemingly distinct issues are connected through the broader theoretical framework and its practical applications. Existing research provides limited cross-country evidence on the effects of robotics adoption on unemployment and environmental outcomes, and little is known about the roles of culture and spatial spillovers in shaping these effects. Based on a relevant theoretical framework, the thesis addresses the gaps by proposing research questions in two consecutive empirical chapters, as explained in the next section. Secondary databases are employed to examine the research questions. In the thesis, the results are drawn from performing multiple regressions on the cross-country empirical data gathered. Briefly, the thesis analyses the impact of the adoption of robotics on unemployment and SO₂ emissions, focusing on revealing the interdependency between economic characteristics and sustainability. Accordingly, it is organised into two empirical chapters. The first empirical chapter investigates the effects of robotics adoption on unemployment across multiple national contexts, utilising panel data from 33 countries over the period 2010–2019. This analysis employs theoretical frameworks of innovation diffusion and absorptive capacity to explore how the adoption of robotics influences international unemployment disparities. Additionally, this chapter examines the employment effects of robotics through a cross-cultural lens. The empirical results indicate that robotics adoption reduces unemployment; however, this effect differs substantially across countries when cultural dimensions are included as moderating factors. The chapter thereby extends the existing body of research on robotics adoption and unemployment by integrating insights from innovation diffusion and absorptive capacity theories. Moreover, the findings offer policymakers deeper insights into the nuanced role of cultural contexts in shaping employment-related policies related to the adoption of robotics. The second empirical chapter focuses on examining the impact of robotic adoption on sulphur dioxide (SO₂) emission intensity (per unit of GDP) and total SO₂ emissions. The analysis applies a comprehensive panel dataset from 50 countries spanning the period from 2000 to 2019. The application of advanced econometric methodologies, with the Spatial Durbin Model (SDM) being a notable example, is situated within the theoretical frameworks of spatial spillover and absorptive capacity. This approach differentiates between domestic and cross-border impacts of robotics on air quality indicators. The empirical evidence suggests that the adoption of robotics is conducive to the reduction of SO₂ intensity and total SO₂ emissions to both domestic and neighbouring countries, thereby demonstrating significant direct and spillover effects that transcend national boundaries. The spatial econometric analysis presented in this chapter makes a significant contribution to the existing literature on technological advancements and environmental outcomes. Furthermore, it has been demonstrated that integrating robotics can enhance understanding of the intricate consequences that arise from this intersection in global environmental governance and policy-making processes. The structure of this thesis deviates from the conventional single, continuous narrative structure, adopting instead two independent empirical research chapters. These chapters have the potential to be published separately in academic journals. This format aligns with emerging trends in interdisciplinary research, which often involve multiple authors across various dimensions of broader research topics. This structure facilitates the rapid dissemination and interaction of academic outcomes within the academic community. Furthermore, it has been demonstrated that this approach can facilitate the timely production of research findings, thereby making an academic contribution to their field.