Currently, I am interested in exploring topics of environment inequality, cascading impact of climate shocks, climate finance, and North-South interactions through trade and migration. I work with macroeconomic models, agent-based models, and causal inference (DiDs, RDDs). I am consistently coding in Stata, Mathematica, and NetLogo. I also regularly publish my code on Medium and Twitter.
Natural disasters negatively impact regions and exacerbate socioeconomic vulnerabilities. While the direct impacts of natural disasters are well understood, the channels through which these shocks spread to non-affected regions, still represents an open research question. In this paper we propose modelling socioeconomic systems as spatially-explicit, multi-layer behavioral networks, where the interplay of supply-side production, and demand-side consumption decisions, can help us understand how climate shocks cascade. We apply this modelling framework to analyze the spatial-temporal evolution of vulnerability following a negative food-production shock in one part of an agriculture-dependent economy. Simulation results show that vulnerability is cyclical, and its distribution critically depends on the network density and distance from the epicenter of the shock. We also introduce a new multi-layer measure, the Vulnerability Rank ( VRank ), which synthesizes various location-level risks into a single index. This framework can help design policies, aimed to better understand, effectively respond, and build resilience to natural disasters. This is particularly important for poorer regions, where response time is critical and financial resources are limited.
@article{Naqvi2021b,author={Asjad Naqvi, Irene Monasterolo},doi={10.1038/s41598-021-99343-4},issn={2045-2322},journal={Scientific Reports},month=dec,number={1},pages={20146},title={{Assessing the cascading impacts of natural disasters in a multi-layer behavioral network framework}},url={https://www.nature.com/articles/s41598-021-99343-4},volume={11},year={2021}}
JCP
Decoupling trends of emissions across EU regions and the role of environmental policies
The paper combines grid-level data of eight emission types –CO2, N20, CH4, NH3, NOX, PM10, PM2.5, and SO2 – with sub-national economic data to create a 1995–2015 balanced panel for NUTS 2 regions in EU countries. Regions on average show decoupling of emissions from output but most of the emission reductions are achieved before the 2008 financial crisis. Post 2008, very weak decoupling and even coupling can be observed. Using OECD’s Environmental Policy Stringency (EPS) Index as an intervention variable, an event study analysis shows that strong policies significantly reduce emissions, but there is considerable heterogeneity in the response by emission types and regional income levels.
@article{Naqvi2021a,title={Decoupling trends of emissions across EU regions and the role of environmental policies},journal={Journal of Cleaner Production},year={2021},issn={0959-6526},doi={https://doi.org/10.1016/j.jclepro.2021.129130},url={https://www.sciencedirect.com/science/article/pii/S0959652621033175},author={Naqvi, Asjad},keywords={Decoupling, Emissions, European Union (EU), NUTS regions, Environmental policy stringency, Difference-in-difference, Event study}}
JFS
Climate sentiments, transition risk, and financial stability in a stock-flow consistent model
A successful low-carbon transition requires the introduction of policies aimed at aligning investments to the climate and sustainability targets. In this regard, a global Carbon Tax (CT) and a revision of the microprudential banking framework via a Green Supporting Factor (GSF) have been advocated but two main knowledge gaps remain. First, the understanding of the conditions under which the CT or the GSF could contribute to the scaling-up of new green investments or, in contrast, could introduce new sources of risk for macroeconomic and financial stability, is poor. Second, we don’t know how banks’ climatesentiments, i.e. their anticipation of climate policies’ impact in lending conditions, could affect the outcomes of the policies and of the low-carbon transition. To fill these knowledge gaps we develop a Stock-Flow Consistent model of a high income country that embeds an adaptive forecasting function of banks’ climate sentiments. Then, we assess the impact of the CT and GSF on the greening of the economy and on the banking sector analyzing the risk transmission channels from the credit market to the economy via loans contracts, and the reinforcing feedbacks that could give rise to cascading effects. Our results suggest that the GSF contributes to scale up green investments only in the short-run but it also introduces potential trade-offs on bank’s financial stability. To foster the low-carbon transition while preventing unintended effects on Non-Performing Loans and households’ budget, the introduction of the CT should be complemented with redistribution welfare policies. Finally, if banks revise their credit supply conditions based on the firms’ carbon profile ahead of climate policy introduction, they can contribute to align investments to the low-carbon transition and improve financial stability of the banking sector.
@article{Dunz2021,title={Climate sentiments, transition risk, and financial stability in a stock-flow consistent model},journal={Journal of Financial Stability},volume={54},pages={100872},year={2021},issn={1572-3089},doi={https://doi.org/10.1016/j.jfs.2021.100872},url={https://www.sciencedirect.com/science/article/pii/S1572308921000322},author={Nepomuk Dunz, Asjad Naqvi, Irene Monasterolo},}
This Tracker presents data on daily COVID-19 cases at the sub-national level for 26 European countries from January 2020 till present. Country-level data sources are identified and processed to form a homogenized panel at the NUTS 3 or NUTS 2 level, the two lowest standardized administrative units in Europe. The strengths and weaknesses of each country dataset are discussed in detail. The raw data, spatial layers, the code, and the final homogenized files are provided in an online repository for replication. The data highlights the spatial distribution of cases both within and across countries that can be utilized for a disaggregated analysis on the impacts of the pandemic. The Tracker is updated monthly to expand its coverage.
@article{Naqvi2021,author={Naqvi, Asjad},doi={10.1038/s41597-021-00950-7},issn={2052-4463},journal={Nature Scientific Data},month=dec,number={1},pages={181},title={COVID-19 European regional tracker},url={http://www.nature.com/articles/s41597-021-00950-7},volume={8},year={2021}}
