Search arXivSearch

arXiv · 2402.01784

GHG emissions in the EU-28. A multilevel club convergence study of the Emission Trading System and Effort Sharing Decision mechanisms

Abstract

The European Union is engaged in the fight against climate change. A crucial issue to enforce common environmental guidelines is environmental convergence. States converging in environmental variables are expected to be able to jointly develop and implement environmental policies. Convergence in environmental indicators may also help determine the efficiency and speed of those policies. This paper employs a multilevel club convergence approach to analyze convergence in the evolution of GHG emissions among the EU-28 members, on a search for countries transitioning from disequilibrium to specific steady-state positions. Overall convergence is rejected, with club composition depending on the specific period (1990-2017, 2005-2017) and emissions categories (global, ETS, ESD) analyzed. Some countries (e.g. the United Kingdom and Denmark) are consistently located in clubs outperforming the EU's average in terms of speed of emissions reductions, for both the whole and the most recent periods, and for both ETS and ESD emissions. At the other end, Germany (with a large industrial and export basis), Ireland (with the strongest GDP growth in the EU in recent years) and most Eastern EU members underperform after 2005, almost reversing their previous positions when the study begins in 1990.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

María José Presno, Manuel Landajo, Paula Fernández González. 2024-02-08. GHG emissions in the EU-28. A multilevel club convergence study of the Emission Trading System and Effort Sharing Decision mechanisms. https://doi.org/10.1016/j.spc.2021.02.032

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Computing Endogenous Transformations in Processing Networks: A Dynamic Calibration Approach

Understanding how supply chains endogenously transform requires a parametric model of processing networks with non-neutral substitution elasticities. While the Cascaded CES production function provides a rigorous framework, dynamically calibrating its structural parameters from time-series data constitutes a highly non-convex inverse optimization problem. Since enforcing strict microeconomic concavity renders standard monolithic approaches computationally intractable, we propose a novel structure-exploiting algorithm to bypass this limitation. By leveraging the physical upstreamness topology of the network, our hybrid heuristic effectively breaks the curse of dimensionality inherent in economywide processing networks. Applying this framework to U.S. time-series data, we provide a scalable computational engine to fully endogenize complex supply-chain transformations, ultimately uncovering the elastic origins of asymmetric macroeconomic tail risks.

econ.GN

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN

Screening Out the Needy: The Effects of SNAP Work Requirements

We examine the effectiveness of work requirements as a screening device in the Supplemental Nutrition Assistance Program (SNAP). Work requirements for "able-bodied adults without dependents" were suspended after the Great Recession and gradually reinstated across counties and states in the 2010s. Using linked administrative SNAP and employment data from five states and a triple-differences design, we find that work requirements reduce SNAP participation by seven percent without increasing labor supply and disproportionately screen out low-income individuals. We develop a welfare framework to interpret these results and find that the social costs of work requirements exceed budget savings.

econ.GN