The Unassuming Economist

From a paper by Charlotte Liotta and Jeroen van den Bergh:

“The long-standing growth-versus-environment debate has centered on national and global scales, devoting little attention to cities despite steadily increasing urban concentrations of population, activities and emissions. This Perspective clarifies how this debate plays out for cities by relating four urban growth dimensions—economic, population, spatial and environmental—to the narratives of green growth, degrowth and post-growth. To this end, we review theoretical and empirical insights about links between growth dimensions. Specific issues addressed include horizontal spillovers among cities, vertical policy integration and local experiments. Thus we connect the abstract growth-versus-environment debate to evidence regarding urban environmental policy.”

From a paper by Boyang Lia, Runze Chena, and Yuqin Dua:

“The power generation mix in China heavily relies on fossil energy sources, impeding
the advancement of clean power generation and emission reduction efforts. This paper
presents a macroeconomic model incorporating Emissions Trading Systems (ETS),
clean energy subsidies, and intertemporal learning behavior. It examines how carbon
pricing and clean subsidy policies influence the power generation sector and emission
reduction goals. The findings indicate that (1) pricing strategies based on total
emissions effectively drive emission reductions but may not adequately incentivize
cleaner energy transitions. (2) Increasing clean energy subsidies encourages a shift
towards cleaner technologies, although the impact on emission reductions is moderate.
(3) Combining both policies proves to be more effective than implementing either one
alone. (4) There exists a gap in understanding the clean power generation industry, with
both policies contributing to knowledge accumulation in this sector. The insights from
this study are valuable for countries employing ETS mechanisms.”

From a paper by Muhammad Ibrahim Shah, Matteo Foglia, Umer Shahzad, and Zeeshan Fareed:

“This paper investigates how oil price, COVID-19, and global energy innovation can affect carbon emissions under time- and frequency-varying perspectives. We contribute to the literature by being the first research to document the relationship between these variables in the short and long run (dynamically) at different frequencies in a multivariate context, thus providing a more detailed picture of the forces driving CO₂ emissions. For this purpose, we use a novel methodology, i.e., the wavelet local multiple correlation (WLMC) recently developed by Polanco-Martínez et al. (2020). The results provide fresh evidence of long-run asymmetric dynamic correlations, highlighting how the oil price plays a key role in the dynamics of CO₂ emissions. Moreover, we find that, during the long period, there is a strong negative co-movement between CO₂ and the global energy innovation index, i.e., more investment in clean energy induces less emission. Supported by our findings, this research suggests crucial policy implications and insights for the governments worldwide in their efforts to revive their economies amidst the pandemic and environmental uncertainties.”

From a paper by Roy Cerqueti, Raffaele Mattera, and Saverio Storani:

“This paper develops a class of complex network-based models whose interconnected nodes are commodities. We assume that the considered commodities are linked on the ground of the similarities of risk profiles and correlations of their returns. In this framework, we explore the resilience of the networks — i.e., their ability to absorb exogenous microscopic shocks. To this aim, we assume that high levels of resilience are associated with small variations of the community structure of the network when an exogenous shock occurs — hence, assuming that the stability of the networked commodities is measured through the maintenance of their connection levels. Shocks are conceptualized as impulsive modifications of the links among the considered commodities. The employed methodological instrument is the clustering coefficient, which is a nodal centrality measure describing the way the adjacent of the nodes are mutually connected. The theoretical proposal is empirically tested over a large set of commodities of different nature.”

From paper by Eliphas Ndou & Nombulelo Gumata:

“This paper estimates various inflation threshold and structural VAR models to investigate the passthrough of oil prices to consumer price inflation in South Africa when inflation is in the 3–6 percent inflation target band compared to when it is above 6 percent. The paper uses monthly data from 2001M1 to 2022M12. We find that the oil price passthrough to inflation is about three times lower when inflation is within the 3–6 percent target band compared to when it is above the 6 percent. Thus, under the inflation targeting framework, the 3–6 percent inflation target band has induced a structural change in the oil price and inflation relationship in South Africa. In addition, a one percentage point oil price inflation shock raises inflation by 0.11 percentage points when inflation is below 4.5 percent compared to 0.43 percentage points above this threshold. These findings imply that the oil price passthrough coefficient in the Bank forecasting model should be half the size when inflation is within 3-6 percent compared to when inflation isabove 6 percent”