Tesi etd-02282023-204931
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Tipo di tesi
Dottorato
Autore
RUGHI, TOMMASO
URN
etd-02282023-204931
Titolo
Essays on technological evolution and socio-economic impacts under climate change
Settore scientifico disciplinare
SECS-P/01
Corso di studi
Istituto di Economia - JOINT PHD IN ECONOMICS
Commissione
relatore Prof. FAGIOLO, GIORGIO
Parole chiave
- Automotive sector
- Climate econometrics
- Climate impacts
- Natural Language Processing
- Patent activities
- Technological evolution
Data inizio appello
19/07/2023;
Disponibilità
parziale
Riassunto analitico
This thesis explores the complex nexus between climate change and socio-economic systems, with a specific focus on technological evolution. The empirical analysis is conducted upon multi-layer newly constructed dataset in order to analyse different system levels processes. Econometric and advanced statistical techniques, including natural language processes and semantic analysis are applied along a multi-level range of domains, from firms, to sectors to the overall macroeconomy.
Chapter 1 start from a micro level perspective, focusing on the firms active in the automotive sector. It deals with the development of low emission vehicles (LEVs) in the sector, as paradigmatic example of green innovation in response to climate change. Moreover the development of LEVs represents a typical case of technological competition between two green trajectories. On the one hand, the incremental trajectory aims at improving the efficiency of the dominant design, greening the internal combustion engine (ICEG). On the other hand, the radical trajectory targets the progress of hybrid, electric and fuel cell vehicles (HEF).
As already mentioned, the chapter studies the innovative behaviours of firms in the automotive sector patenting in both green trajectories. It investigates the extent to which technological leadership in green patents is rooted in firms’ knowledge and capabilities accumulated in brown domains. Using a novel dataset of automotive firms with patenting activity at the United States Patent and Trademark Office (USPTO) between 2001 and 2018, it finds that related “brown knowledge” denotes leadership in green trajectories, in line with more aggregated studies that find a complementarity between brown and green knowledge. The chapter also investigates the role of technological diversification knowledge for achieving the technological leadership in both green trajectories for the firms: the results show that technological diversification knowledge does exert a role, but less crucial than the one of brown knowledge. Overall the chapter draw a picture of the technological landscape, using both clustering technique analysis and multinomial logistic models as empirical and econometric tools.\\
Chapter 2 moves to the meso-level, investigating the overall green patent activities in US across sectors, and focusing on second order climate impacts, namely the unintended consequences/impacts propagating from mitigation/adaptation strategies, policies and technologies. The focus of the chapter is to provide a direct understanding of the twin transition from the innovative activity domain. The chapter starts with a technological mapping of the technological innovations characterised by both climate change mitigation/adaptation (green) and labour-saving attributes. To accomplish the task, it draws on the universe of patent grants in the USPTO since 1976 to 2021 reporting the Y02-Y04S tagging scheme, defined by the USPTO as patents referring to green technologies. After such identification, by means of a textual-content algorithm, the chapter identifies those patents reporting an explicit labour-saving heuristic. It also characterise their technological, sectoral and time evolution and their overall technological penetration. Finally, the chapter explores their impact on employment share at state level in US, using SUSB data. In a nutshell, the findings challenge the common understanding of the “green transition” as only labour augmenting.\\
Finally chapter 3 adopts a macro-level approach, tracing the more recent contributions in the climate econometric literature. The aim of the chapter is to better characterize the dimension of heterogeneity of climate impacts on different macroeconomic channels, testing their robustness and highlighting possible limitations. It expand the previous contributions on two grounds: first it consider a wider list of variables considered in the analysis as dimensions of climate impacts. Second it test numerous econometric specifications in order to retrieve specific aspects of impacts, as the role if income group, or the effects of longer time dimensional climate impacts. Intersecting these two dimensions, the chapter documents the following results: first, only a subset of macro-variables (per capita Consumptions and productivity measures, both labour and agricultural ones), are robustly and significantly affected by climate variables across the econometric specification, in addition to per capita GDP. Second, specific variables, such as the innovation variables, show to be affected by climate change only if longer time structures of impacts are considered. Thirdly, with the empirical strategy adopted, precipitation impacts at macro-level also acquire a relevant role. Fourth, the chapter documents an overall lower level of climate impacts on TFP, thus challenging its use as meaningful macro-economic variable to be tested versus climate change. Finally, however, one the most striking results involve the irreducible heterogenity documented by the chapter and the intrinsic limits of very aggregated spatial and temporal data.
Chapter 1 start from a micro level perspective, focusing on the firms active in the automotive sector. It deals with the development of low emission vehicles (LEVs) in the sector, as paradigmatic example of green innovation in response to climate change. Moreover the development of LEVs represents a typical case of technological competition between two green trajectories. On the one hand, the incremental trajectory aims at improving the efficiency of the dominant design, greening the internal combustion engine (ICEG). On the other hand, the radical trajectory targets the progress of hybrid, electric and fuel cell vehicles (HEF).
As already mentioned, the chapter studies the innovative behaviours of firms in the automotive sector patenting in both green trajectories. It investigates the extent to which technological leadership in green patents is rooted in firms’ knowledge and capabilities accumulated in brown domains. Using a novel dataset of automotive firms with patenting activity at the United States Patent and Trademark Office (USPTO) between 2001 and 2018, it finds that related “brown knowledge” denotes leadership in green trajectories, in line with more aggregated studies that find a complementarity between brown and green knowledge. The chapter also investigates the role of technological diversification knowledge for achieving the technological leadership in both green trajectories for the firms: the results show that technological diversification knowledge does exert a role, but less crucial than the one of brown knowledge. Overall the chapter draw a picture of the technological landscape, using both clustering technique analysis and multinomial logistic models as empirical and econometric tools.\\
Chapter 2 moves to the meso-level, investigating the overall green patent activities in US across sectors, and focusing on second order climate impacts, namely the unintended consequences/impacts propagating from mitigation/adaptation strategies, policies and technologies. The focus of the chapter is to provide a direct understanding of the twin transition from the innovative activity domain. The chapter starts with a technological mapping of the technological innovations characterised by both climate change mitigation/adaptation (green) and labour-saving attributes. To accomplish the task, it draws on the universe of patent grants in the USPTO since 1976 to 2021 reporting the Y02-Y04S tagging scheme, defined by the USPTO as patents referring to green technologies. After such identification, by means of a textual-content algorithm, the chapter identifies those patents reporting an explicit labour-saving heuristic. It also characterise their technological, sectoral and time evolution and their overall technological penetration. Finally, the chapter explores their impact on employment share at state level in US, using SUSB data. In a nutshell, the findings challenge the common understanding of the “green transition” as only labour augmenting.\\
Finally chapter 3 adopts a macro-level approach, tracing the more recent contributions in the climate econometric literature. The aim of the chapter is to better characterize the dimension of heterogeneity of climate impacts on different macroeconomic channels, testing their robustness and highlighting possible limitations. It expand the previous contributions on two grounds: first it consider a wider list of variables considered in the analysis as dimensions of climate impacts. Second it test numerous econometric specifications in order to retrieve specific aspects of impacts, as the role if income group, or the effects of longer time dimensional climate impacts. Intersecting these two dimensions, the chapter documents the following results: first, only a subset of macro-variables (per capita Consumptions and productivity measures, both labour and agricultural ones), are robustly and significantly affected by climate variables across the econometric specification, in addition to per capita GDP. Second, specific variables, such as the innovation variables, show to be affected by climate change only if longer time structures of impacts are considered. Thirdly, with the empirical strategy adopted, precipitation impacts at macro-level also acquire a relevant role. Fourth, the chapter documents an overall lower level of climate impacts on TFP, thus challenging its use as meaningful macro-economic variable to be tested versus climate change. Finally, however, one the most striking results involve the irreducible heterogenity documented by the chapter and the intrinsic limits of very aggregated spatial and temporal data.
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