High levels of air pollution reduce team performance

Teams play a key role in tackling complex societal challenges, such as developing vaccines or novel clean energy technologies. Yet, the effect of air pollution on team performance in non-routine problem-solving tasks is not well explored. Here, we document a sizable adverse effect of air pollution on team performance using data from 15,000 live escape games in London, United Kingdom. On high-pollution days, teams take on average 5% more time to solve a sequence of non-routine analytical tasks, which require collaborative skills analogous to those needed in the modern workplace. Negative effects are non-linear and only occur at high levels of air pollution, which are however commonplace in many developing countries. As team efforts predominantly drive innovation, high levels of air pollution may significantly hamper economic development

The Economics of the Clean Energy Transition – An Empirical Analysis of Novel Policies for the Development and Deployment of Climate Change Mitigation Technologies

Market forces alone will likely provide insufficient incentives for achieving the goals of the Paris Agreement; policies are thus playing a key role in accelerating this transition. As the energy sector is responsible for around two thirds of global emissions, understanding the effectiveness of existing and novel policies for the development and deployment of climate change mitigation technologies is critical. This thesis is oriented along three key energy-related challenges: First, how can industrialised countries develop and spread novel low-carbon technologies? To that end, we develop a new approach to trace the development of scientific articles into patented technologies, relying on machine learning, various data science procedures, and econometrics (Chapter 2). With this approach, we then show – using data from 102,301 scientific publications on biofuels, li-ion batteries, solar PV, and wind energy – that open access scientific articles (i.e., those made available free of cost) are more likely to be used in patents (Chapter 3). Moreover, we demonstrate that geographical proximity between scientists and inventors and content similarity between the scientific publication and the patent reduce the diffusion time between the publication of the paper and the patent (Chapter 4). We thereby shed light on the geographic and semantic characteristics of knowledge spillovers. Second, we investigate how industrialising countries can localise a greater part of the value chain in clean energy technologies to meet economic and environmental goals? We analyse the factors that lead to the eventual deployment of these technologies in developing and emerging economies. Using detailed country-level data on two emerging economies, South Africa (Chapter 5) and India (Chapter 6), we provide the first empirical assessment of the short-term cost and benefits of local content requirements in renewable energy auctions, using a credible counterfactual. Third, we also investigate: how can developing countries crowd in financing for renewable energy? We provide evidence on the design and socioeconomic impacts of an internationally supported renewable energy deployment scheme in Uganda (Chapter 7) to demonstrate that de-risking policies to crowd in private sector investment are a promising alternative to current schemes.Department of Land Economy Heinrich Böll Foundation German National Academic Foundatio

Leveraging large language models to monitor climate technology innovation

To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy

Leveraging large language models to monitor climate technology innovation

To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy.ISSN:1748-9326ISSN:1748-931

Leveraging large language models to monitor climate technology innovation

To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy

Leveraging large language models to monitor climate technology innovation

To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy

The effectiveness of building retrofits under a subsidy scheme: Empirical evidence from Switzerland

While retrofitting buildings is one of the key elements of reaching climate and energy goals, it is burdened by insufficient speed and depth. Governments have attempted to accelerate deep retrofits via subsidies, but scant evidence exists on these policies’ effectiveness. In this study, we investigate the effectiveness of retrofitting subsidies by using a range of econometric techniques and a unique dataset of over 400 Swiss buildings with 19,000 observations over 11 years. Specifically, we analyze whether retrofits reduce energy consumption, whether subsidized retrofits lead to deeper retrofits than non-subsidized retrofits, and we differentiate the impact by subsidy amount. We find that retrofits reduce average energy use by 10–20%, that the achieved savings through subsidized and non-subsidized retrofits do not differ significantly, and that the subsidy amount is correlated to a reduction in energy use by 0.42 CHF per kWh over a period of 20 years. Our study highlights the importance of policies that enhance retrofit depth, the need to further investigate the causes of the wide variation in retrofitting results, and to consider effectiveness studies within the policy design.ISSN:0301-421

Global trends in the invention and diffusion of climate change mitigation technologies

Increasing the development and diffusion of climate change mitigation technologies on a global scale is critical to reaching net-zero emissions. We have analysed over a quarter of a million high-value inventions in all major climate change mitigation technologies patented from 1995 to 2017 by inventors located in 170 countries. Our analysis shows an annual growth rate of 10% from 1995 to 2012 in these high-value inventions. Yet, from 2013 to 2017, the growth rate of these inventions fell by around 6% annually, likely driven by declining fossil fuel prices, low carbon prices and increasing technological maturity for some technologies, such as solar photovoltaics. Invention has remained highly concentrated geographically over the past decade, with inventors in Germany, Japan and the United States accounting for more than half of global inventions, and the top ten countries for almost 90%. Except for inventors in China, most middle-income economies have not caught up and remain less specialized in low-carbon technologies than high-income economies

High levels of air pollution reduce team performance

Teams play a key role in tackling complex societal challenges, such as developing vaccines or novel clean energy technologies. Yet, the effect of air pollution on team performance in nonroutine problem-solving tasks is not well explored. Here, we document a sizable adverse effect of air pollution on team performance using data from 15,000 live escape games in London, United Kingdom. On high-pollution days, teams take on average 5% more time to solve a sequence of non-routine analytical tasks, which require collaborative skills analogous to those needed in the modern workplace. Negative effects are non-linear and only occur at high levels of air pollution, which are however commonplace in many developing countries. As team efforts predominantly drive innovation, high levels of air pollution may significantly hamper economic development