Nikolas Kuschnig

Hi, I'm Nikolas Kuschnig, a PhD graduate in Economics at the Vienna University of Economics and Business (WU). I am on the 2024–2025 job market.

My research interests lie in environmental economics and applied econometrics, with a focus on deforestation and spillover effects. Contact me via mail , check out my CV , my research, teaching, or other work.

Recent Work

I am currently working on my job market paper on spillovers and the networks behind them. Most recently, we finished two working papers related to mining operations.

In my job market paper, Networks in Space, I develop a joint model of spillovers and the latent networks behind them. I investigate the deforestation impacts of prioritizing deforestation hotspots in the Brazilian Amazon, and find that conventional methods underestimate spillovers considerably. By recovering the centrality of regions in the deforestation network, my approach can help reveal effective intervention targets.

Networks in Space — Spillovers in Amazon Deforestation. WiP Slides

Abstract.

Spillover effects between regions are common in deforestation and (environmental) economics. Yet, data on the networks behind them is elusive, and empirical analyses rely on proxies and assumptions. In this paper, I present a hierarchical approach to jointly model spillovers and the latent networks driving them. I apply this approach to investigate the deforestation impact from blacklisting municipalities in the Brazilian Amazon. I find large positive spillovers from the intervention that are underestimated considerably when using conventional spatial proxies. Results suggest that endogenous spillovers from complementary effects cannot be ignored when assessing deforestation. My approach is widely applicable to assess regional spillovers; its flexibility can improve our understanding of spillovers – revealing the networks behind them, and enabling more targeted, effective interventions.

Figure: The Brazilian 'Legal Amazon' and areas that were cleared over 2004–2022.

Map of deforestation within the Brazilian Legal Amazon. Deforestation is clearly clustered close to previously cleared areas along the 'arc of deforestation' and along new and established roads.

Deforestation in the Brazilian Amazon continues to be an issue, and vast areas have been cleared over the past two decades. In the map, the historical 'arc of deforestation' along the borders of the Amazon biome (ranging from the Southwest to the Northeast) and the stretches of deforestation in the center (emanating from roads) are clearly visible. These deforestation clusters suggest a strong spatial dimension of deforestation and its drivers.

Figure: Estimates of centrality from a model with individual specific connectivity.

Map of Brazilian municipalities in the Legal Amazon and estimates of their centrality from a flexible model. Frontier regions have much higher centrality, reaching over 50 times the average.

Here we see estimates of municipalities' outward centrality (relative) from a model that allows for individual-specific connectivity (degree heterogeneity). The smaller northeastern regions (that would usually be central by design) are overshadowed by regions at the Amazon's frontier. This model is still restrictive due to the assumption of known (latent) positions at the centroids of municipalities, and thus isolates larger municipalities (deep in the Amazon) comparatively to smaller ones (in the Northeast). Flexible models that reveal such central regions can help target interventions more effectively.

In Mines–Rivers–Yields, we show that mines in Africa negatively affect agricultural yields and the health of natural vegetation downstream. The concentrated value of extracted minerals is offset by the reduction of ecosystem services for a large population downstream of mines. Such externalities threaten potential benefits from a mineral boom, particularly in a development context, and need to be addressed urgently.

Mines–Rivers–Yields: Downstream Mining Impacts on Agriculture in Africa. (with Vashold, L., Pirich, G., Heinze, M.) WP

Abstract.

Minerals are essential to fuel the green transition, can foster local employment and facilitate economic development. But their extraction is also linked to several negative social and environmental externalities. Our understanding of these adverse impacts is especially limited in low-income countries, undermining our ability to manage them. In this paper, we use remotely sensed data to shine a light on the impacts of mine runoffs on agricultural yields in Africa. We use a discontinuity from mine locations along river basins to identify the causal effect that is mediated by water. Both agricultural yields and vegetation health in general are reduced considerably downstream of mines. Effects are economically significant and dissipate after about 100 kilometers, suggesting sediment as a possible cause. Our findings underscore an urgent need to better address polluting industries in a development context to limit their negative externalities for agriculture.

Figure: Illustration of the vegetation discontinuity along river basins.

Scatterplots, repeated in four panels, that display a sharp discontinuity in vegetation around and downstream of mining sites.

Here we see the enhanced vegetation index (EVI) plotted against the distance from the mine location (measured by the order of river basin) around four selected mines (for multiple years). Our analysis is focused on impacts that are mitigated by water, i.e., at and downstream of mine locations. This is most clearly visible for the Angolan mine (top-left); by contrast, data for the mine in Lesotho (top-right) suggests the presence of other potential impacts that also affect vegetation upstream.

In Mapping Mining Areas in the Tropics from 2016–2024, we present a longitudinal (panel) dataset of mining sites. Information on mine locations and their extent have been scarce, limiting analyses of their socioeconomic and environmental impacts. We use a segmentation model to automatically delineate mining areas in satellite images, allowing us to easily segment and closely track mining sites using historical and up-to-date images.

Mapping Mining Areas in the Tropics from 2016–2024. (with Sepin, P., Vashold, L.) WP

Abstract.

Mining provides crucial materials for the global economy and the climate transition, but has potentially severe adverse environmental and social impacts. Currently, the analysis of such impacts is obstructed by the poor availability of data on mining activity — particularly in regions most affected. In this paper, we present a novel panel dataset of mining areas in the tropical belt from 2016 to 2024. We use a transformer-based segmentation model, trained on an extensive dataset of mining polygons from the literature, to automatically delineate mining areas in satellite imagery over time. The resulting dataset features improved accuracy and reduced noise from human errors, and can readily be extended to cover new locations and points in time as they become available. Our comprehensive dataset of mining areas can be used to assess local environmental, social, and economic impacts of mining activity in regions where conventional data is not available or incomplete.

Figure: Segmentation of the Toka Tindung gold mine over time (Planet/NICFI).

Satellite image of the Toka Tindung gold mine in Indonesia from 2016, 2020, and 2024 and our prediction of its increasing extent.

Here we see the expansion of the Toka Tindung mine (Maps ), one of the largest gold mines in Southeast Asia, in Indonesia over time. Our predictions capture the expansion of the main pit, and the merging of the previously disconnected Toka pit in the North and the Kopra, the Blambangan, and the Araren pits in the South.