JBNST - Vol. 244/3 - 2024

This paper employs a dynamic multi-sector growth model with changing technology to study the relevance of the price and quantity dimensions involved in the technical substitution of carbon-intensive technology, that is, the low-carbon transition. For the framing of the transition, the stylized market dynamics by Flaschel and Semmler (1987. “Classical and Neoclassical Competitive Adjustment Processes.” The Manchester School 55 (1): 13–37) are used, who propose a cross-dual out-of-equilibrium adjustment process. The major empirical challenge to identify the adjustment speed for quantities and prices is to empirically estimate sector-specific adjustment coefficients. The transition speed is estimated for seven carbon-intensive sectors in six high-income economies (Germany, France, Italy, Netherlands, Japan, and the US) using a mixed-effects varying-slopes model on EU KLEMS data. Directed technical change is enforced by a revenue-neutral, pro-active fiscal policy of a tax–subsidy form, which has the effect to greatly accelerate the phase-out of carbon-intensive technology and the phase-in of green technology. The speed of green substitution that allows decarbonization is then evaluated analytically and computationally along four policy and time dimensions: cost advantage, a percentage tax on carbon-intensive output, a green subsidy rate, and initial investment ratios. Though the tax itself has an impact on the speed of decarbonization, it is significantly improved by green subsidies and green investments. The cost advantage of the green over the carbon technology is shown to have a negligible impact on decarbonization speed by itself. Without ambitious fiscal policy, especially in the form of green investment support, this substitution process appears to be too slow to reach decarbonization in a timely manner.

Using a newly collected dataset at the plant level from 2014 to 2018, we provide the first microscopic portrait of robotization in Germany and study the correlates of robot adoption. Our descriptive analysis uncovers five stylized facts: (1) Robot use is relatively rare. (2) The distribution of robots is highly skewed. (3) New robot adopters contribute substantially to the recent robotization. (4) Robot users are exceptional. (5) Heterogeneity in robot types matters. Our regression results further suggest plant size, high-skilled labor share, exporter status, and labor shortage to be strongly associated with the future probability of robot adoption.

Since the onset of the COVID-19 pandemic, many studies have appeared on how it will affect climate change and policy, and vice versa. In this study, we systematically review the literature about this relationship. We obtained a sample of 204 articles published in the period from February 2020 to March 2022. Using topic modelling and qualitative analysis, we identify seven main topics on the nexus of COVID-19 and climate change: (1) impacts of COVID-19 on climate change and policy; (2) sustainable recovery after COVID-19; (3) public concern about climate change and COVID-19; (4) lessons from COVID-19 for climate change action; (5) effects of COVID-19 and climate change on the economy, food security and poverty; (6) collective responses to crises and (7) similarities and differences between COVID-19 and climate change. We discuss lessons derived from each of these topics and identify key policies and strategies for a sustainable recovery from COVID-19. Investments in low-carbon technologies and carbon taxation are the most commonly suggested policy solutions. If climate targets are to be met, climate action needs to be the focus of COVID-19 recovery. Highlights – We review 204 articles on the relationship between COVID-19 and climate change. – They are classified into seven main topics using computation linguistics. – Studies give most attention to the impacts of COVID-19 on emissions. – Low-carbon technologies and carbon taxation are the most commonly suggested policies.

The Bundesbank Online Panel – Firms (“BOP-F”) is a dataset with responses from a high frequency firm-level survey of the same name. The Bundesbank has conducted the survey since June 2020, and since July 2021 the survey has been carried out at a monthly frequency. Every month, around 3000 firms from all economic sectors, regions and size classes are surveyed. The survey consists of recurring core questions about the economic situation of firms and their expectations and special questions that usually differ from quarter to quarter. The latter often relate to current topics, for instance, climate change, digitalization, Covid-19. The data can be accessed for research and especially the possibility to combine it with other administrative Bundesbank data makes it particularly valuable for research. The objective of this paper is to describe the methodology of the data collection, the content data as well as the data’s research potential.

The paper describes the “Halle Institute for Economic Research (IWH) Forecasting Dashboard (ForDas)”. This tool aims at providing, on a non-commercial basis, historical and actual macroeconomic forecast data for the Germany economy to researchers and interested audiences. The database renders it possible to directly compare forecast quality across selected institutions and over time. It is partly based on data collected in the DFG-funded project “Macroeconomic forecasts in great crisis”.