Refine
Year of publication
Document Type
- Article (1474)
- Part of a Book (927)
- Working Paper (697)
- Editorship book (180)
- Contribution to a Periodical (176)
- Book (140)
- Doctoral Thesis (103)
- Review (45)
- Conference Proceeding (33)
- Journal (18)
Language
- English (2980)
- German (780)
- French (48)
- Spanish (26)
- Other (11)
- Italian (7)
- Dutch (2)
- Multiple languages (1)
- Portuguese (1)
- Russian (1)
Keywords
- Centre for Sustainability (25)
- Germany (24)
- - (20)
- Centre for Fundamental Rights (18)
- China (16)
- Social entrepreneurship (13)
- European Union (12)
- Fertility (12)
- Außenpolitik (10)
- social innovation (10)
Comparative political economy research of the last three decades was based on the analysis of supply side institutions of advanced industrialized countries – the so-called Varieties of Capitalism perspective. A new edited volume by Baccaro et al. (2022) complements the approach by a new demand side perspective. It grows out of a fine-grained analysis of the deregulation of labour markets and loss of power of trade unions in the Western world. While conceptualizing the role of demand for economic growth enriches our understanding, only a research program that combines the demand and supply side will fulfil the expectations of explaining modern economies from a comparative political economy perspective.
To tackle the climate crisis, cities must reduce greenhouse gas (GHG) emissions rapidly. To aid these efforts, many cities are interested in leveraging artificial intelligence and machine learning (ML). Researchers and practitioners, however, only begin to understand how ML can contribute to achieving climate targets in urban contexts. To provide an overview of application areas, and the potential leverage of ML to reduce GHG emissions, we systematically map research conducted over the past three decades. We identify 1,206 relevant peer-reviewed records, and discover that research involving ML is expanding more rapidly than the literature on urban climate mitigation more broadly. The research focus largely aligns with urban mitigation options that the Intergovernmental Panel on Climate Change assessed as having high impact. We also find that research concentrates on the ML-strong regions Eastern Asia, Europe, and Northern America. This regional focus can influence research agendas, and we observed signs that this can lead to bias regarding which ML applications are pursued in urban climate action.
Local governments control a hidden flow of economic goods that never appear on city budgets. Through the housing entitlement process, city officials may condition approval on the benefits developers provide to organized interests. But the politics and policies created by this discretionary review have yet to be studied through the lens of interest group mobilization. We bridge this gap with an analysis of the behavior of construction unions in the housing entitlement process. Using data from 164 U.S. cities, we find that construction union representatives are more likely to attend public meetings to advocate for favorable labor agreements when the expected profitability of new housing developments is high — and thus, when there are more particularistic benefits on the table. While interest group competition within local participatory institutions may signal a robust, pluralist democracy, it also risks driving up housing costs, to the detriment of both organized and unorganized residents.
Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.
Decarbonization involves a large-scale expansion of low-carbon generators such as wind and solar and the electrification of heating and transport. Both space heating and battery-electric cars have significant embedded flexibility potential. Granular price signals that convey abundance or scarcity of electricity are a precondition for customers or aggregators acting on their behalf to exploit this flexibility. However, unmitigated real-time prices expose customers to electricity price risks. To tackle the dual need of providing flexibility incentives while protecting customers from cost shocks, real-time tariffs with a hedging component can be a solution. In such contracts customers pre-agree an amount of energy and a consumption profile, while hourly deviations are charged at spot prices. In this paper we analyze design options by using a dataset of anonymized smart meter data and show that profile tariffs can bring electricity bill volatility to similarly low levels as fixed tariffs while providing full flexibility incentives from spot prices.
