Conference Agenda

In the pursuit of global net-zero emissions by 2050, the Russia–Ukraine War emerges as a potential disruptor, challenging progress toward this critical objective. We explore the repercussions of the conflict on the European Union’s (EU) initial energy transition goals, with a keen focus on electricity generation. Analyzing projections for coal, natural gas, nuclear, hydro, and renewables in Germany and Italy over the postwar period (2023–2027), the study unveils divergent energy landscapes influenced by the conflict’s aftermath. Germany faces hurdles in carbon neutrality given a decline in nuclear power and a surge in coal usage, necessitating the urgent deployment of renewable energy sources. Unexpected rises in natural gas supply emphasize the need for diversified sources, reinforcing energy security. Italy’s resilient energy shifts, marked by hydropower fluctuations and increased renewable energy, suggest continued measures for emission reduction. Recommendations for Italy encompass bolstering hydropower infrastructure, sustaining renewable investments, strategizing natural gas imports, and embracing integrated energy planning. This study not only identifies contrasting energy challenges postwar but also proposes nuanced policy implications tailored to each country’s context, providing valuable insights for navigating the complex path toward sustainable and resilient carbon neutrality amidst geopolitical uncertainties.

We examine the natural resource dependence-income inequality links with the employment of novel methods in the resource’s economics literature of Wavelet Analysis. This conceptually and methodologically adds to the ongoing research in the field by considering volatility and co-movement patterns in the variables which traditional statistical methods often overlook. Income inequality-resource dependence relationships are examined by looking more closely at the role of political regimes and global economic shocks, which have been examined less in the literature to date. Results confirm the volatile natural resource dependence- income inequality co-movement. Significant volatility and co-movement are identified in periods of energy crises or global shocks, such as the energy crisis of the 1970s, the Iran-Iraq conflict in the 1980’s, the Gulf War in 1990, the early 2000s oil price hike, and the global financial crisis of 2008. Democratic regimes relate to income inequality-resource dependence in periods of energy crises. Non-democratic regimes record significant volatility and co-movement at the outbreak of the latest financial crisis in 2008. The analysis contributes to the scientific debate on the resource dependence-income inequality relationship by revealing the complexity of the relationship across different time and frequency domains, and by shedding more light on the role of political regimes and global economic shocks. The policy implications of this work extend to a more nuanced description of natural resource dependence-income inequality links, providing better informed, scientifically driven development policies in resource- dependent countries.

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity – both proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.

Formulating and implementing climate policy requires a detailed understanding of likely pathways of future carbon emissions. However, most existing tools to provide forecasts for such emissions exhibit several limitations. Projections are predominantly made for the long-term, lacking the necessary detail within the typical political policy horizon of one to five years. Moreover, most short-term forecasting models are closed-source ‘black-boxes’ that do not reveal in detail how the obtained forecasts are generated. This paper develops a generalised framework for modelling climate and environmental policies within an empirical macro-econometric modelling approach that is standardised for a large number of countries but allows for immense flexibility. The “Aggregate Model” is an opensource econometric model builder that primarily aims to provide robust empirical forecasts of sectoral carbon emissions that is based on econometric tools from the robust time series modelling literature such as diagnostic testing, indicator saturation, and automatic forecast evaluation. In this paper, we show an illustrative example of how this tool can be used to model and forecast different sectoral emissions in Austria. By putting open-source transparency and replicability at the heart of this project, we contribute to opening up the existing forecasting space to wider academic discussion, criticism, and improvement and hope to provide crucial tools to policy-makers.