Air transport is a key driver of economic development, tourism, and regional connectivity, yet its growth generates increasing environmental costs. Grounded in the catalytic effects framework and the sustainability trade-off perspective, this exploratory study examines the economic and sustainability dimensions of air traffic recovery and growth at Belgrade Nikola Tesla Airport during 2019–2024, a period encompassing a pandemic shock and record post-pandemic expansion. Descriptive statistical analysis and Pearson correlation analysis were applied to six annual data points, supplemented by an approximate CO 2 emission estimation. Passenger traffic increased from 6.16 to 8.37 million (+35.9%), and the destination network expanded from 99 to 135 routes. A positive co-movement was observed between passenger traffic and foreign tourist arrivals (r = 0.970; p = 0.001). No detectable association was found between passenger traffic and annual GDP growth rate (r = 0.143; p = 0.79). Estimated CO 2 emissions grew proportionally from 0.831 to 1.130 million tonnes, consistent with the proportional growth pattern generated by the fixed-factor estimation framework applied. The passengers-per-movement ratio improved from 87.5 to 97.2, indicating a proximate improvement in operational efficiency. These preliminary findings provide exploratory evidence relevant to Sustainable Development Goals 8 and 9 and may inform future research and policy discussions on the sustainability dimensions of airport development. 1. Introduction Therefore, this study aims to assess the economic and sustainability dimensions of air traffic recovery and growth at Belgrade Nikola Tesla Airport during 2019–2024 by: (1) analysing the evolution of key traffic indicators and operational efficiency; (2) examining the statistical association between passenger traffic, foreign tourist arrivals and Gross Domestic Product (GDP) growth in Serbia; and (3) estimating CO 2 emissions attributable to passenger traffic and interpreting the findings within the SDG 8, SDG 9 and SDG 13 framework as an interpretative narrative lens. The study does not seek to establish causal relationships [ 19, 20]; instead, it identifies associations interpretable within the catalytic aviation effects framework, with implications for sustainable transport policy in small transition economies. The paper is organised as follows: Section 2 presents a review of the relevant literature, covering aviation economics and catalytic effects, the environmental dimension of air transport, and the theoretical framework underpinning the study. Section 3 describes the data sources and analytical methods applied. Section 4 presents the results, including traffic recovery indicators, correlation analysis, and CO 2 emission estimation. Section 5 discusses the findings in a comparative and sustainability perspective and addresses the limitations of the study. Section 6 provides conclusions and directions for future research. 2. Literature Review 2.1. Aviation Economics and Catalytic Effects The literature consistently warns that distinguishing correlation from causality is methodologically critical: airports may stimulate development, but more developed regions also generate higher aviation demand [ 19, 20]. Panel data models, instrumental variables, and natural experiment designs are the preferred approaches for causal identification. This paper does not employ these methods due to data constraints, and therefore, does not assert causal relationships; instead, it identifies statistical associations interpretable within the catalytic effects framework. For the Serbian context, Kuljanin and Kalić [ 25] and Božičković and Vilke [ 26] provide directly relevant empirical reference points on passenger dynamics and the growth–sustainability tension in regional aviation, while Graham [ 4] and Burghouwt [ 5] offer the airport management and connectivity frameworks applied here. 2.2. Environmental Dimension and Decarbonisation The environmental dimension of aviation is gaining increasing scientific and policy salience. Lee et al. [ 7] estimate that aviation contributes 3.5–5% of global anthropogenic radiative forcing when non-CO 2 effects of condensation trails and high-altitude phenomena are included. The International Council on Clean Transportation (ICCT) [ 27] reports an average emission factor of 90 g CO 2 per passenger-kilometre for 2019. At the sectoral level, Lenzen et al. [ 28] quantified global tourism’s carbon footprint at 4.5 Gt CO 2-e in 2013, accounting for approximately 8% of global GHG emissions; Sun et al. [ 29] subsequently show that this footprint grew at 3.5% per annum through 2019, more than double the rate of the global economy, driven by demand growth outpacing efficiency gains. Bergero et al. [ 8, 30] conclude that aircraft fuel efficiency improvements alone are insufficient to offset volume growth, and Gössling and Humpe [ 31] identify the structural barriers—regulatory, economic, and technological—that make net-zero aviation highly uncertain without radical policy intervention. 2.3. Theoretical Framework: Catalytic Effects and Sustainability Trade-Offs This study adopts the catalytic effects framework as its primary theoretical foundation [ 1, 4]. According to this framework, aviation’s most significant economic contribution in smaller economies operates through indirect and induced channels, particularly inbound tourism and business mobility, rather than through the direct employment and output of the aviation sector itself. The framework predicts a positive association between connectivity indicators (such as passenger traffic and route network size) and tourism outcomes, while cautioning against interpreting this association as evidence of unidirectional causality. Given the exploratory nature of the study and the limited number of annual observations, the following research expectations are examined descriptively rather than formally tested: RE1: Passenger traffic at BEG in 2019–2024 is positively associated with foreign tourist arrivals in Serbia. RE2: Passenger traffic at BEG shows no detectable association with Serbia’s annual GDP growth rate in the short run. RE3: The fixed-factor emission estimation framework applied in this study is expected to produce proportional growth between estimated emissions and passenger traffic, enabling an illustrative discussion of aviation sustainability trade-offs. RE4: Post-pandemic recovery at BEG is accompanied by a proximate improvement in operational efficiency, proxied by the passengers-per-movement ratio. The reviewed literature collectively establishes that aviation generates significant catalytic economic effects, particularly in smaller and tourism-dependent economies, while simultaneously imposing growing environmental costs, yet empirical evidence for emerging European markets remains limited. The present study contributes an exploratory descriptive analysis of Belgrade Nikola Tesla Airport, drawing on the catalytic effects framework and the sustainability trade-off perspective, with findings interpreted as preliminary descriptive evidence intended to motivate future research. 3. Materials and Methods The study is based on a single-country, single-airport case study of Belgrade Nikola Tesla Airport, Republic of Serbia. This design is appropriate for capturing the dynamics of a specific market through a period of acute external shock and recovery, though it limits the generalisability of findings to other contexts. The primary data sources are the official Annual Activity Reports of BEG for 2019–2024 [ 11, 12, 13, 14, 15, 16], which contain aggregate annual data on passenger traffic, aircraft movements, cargo volume, number of airlines, and destination count. Additional sources are Air Serbia corporate press releases on 2024 operational and financial results [ 36, 37], the International Air Transport Association (IATA) value of aviation report for Serbia [ 2], annual foreign tourist arrival statistics from the Statistical Office of the Republic of Serbia (RZS) [ 38], and annual GDP growth data from the World Bank Open Data database [ 39]. The reliance on official aggregate sources minimises data reliability risks but precludes disaggregation by route, passenger type, nationality, or travel purpose. The methodology is structured in four exploratory analytical layers. The first layer applies descriptive statistical analysis to annual traffic indicators (RE4). Recovery indices were calculated as the ratio of each year’s value to the 2019 baseline, multiplied by 100. The passengers-per-movement indicator was derived by dividing total annual passengers by total aircraft movements reported in official airport statistics, which include all commercial, cargo, and positioning movements, and serves as a proxy for commercial passenger efficiency. The indicator should not be equated with airline load factor, as cargo and non-commercial movements are a small share of total BEG movements; the ratio provides a reasonable order-of-magnitude approximation of capacity utilisation on passenger flights. The second layer applies Pearson correlation analysis (RE1 and RE2) to three annual time series for 2019–2024: passenger traffic at BEG, foreign tourist arrivals in Serbia [ 38], and annual GDP growth rate [ 39]. The Pearson coefficient (r) and corresponding p-value were calculated using Python 3.11. Given the very small number of annual observations ( n = 6), the correlation analysis is interpreted as exploratory and descriptive only. The coefficients are used to illustrate temporal co-movement patterns rather than to provide formal inferential statistical evidence or hypothesis validation. It is important to note that the small sample size ( n = 6) severely limits the statistical power of this analysis and makes results sensitive to structural outliers, particularly the pandemic-year observations of 2020 and 2021. The correlation coefficients are therefore reported as descriptive indicators of co-movement rather than as evidence of stable causal relationships [ 19, 20]. The shared structural break of the COVID-19 crisis mechanically inflates co-movement between all variables that contracted in 2020 and recovered thereafter, which must be considered when interpreting the results. The analysis does not attempt to isolate the effects of the COVID-19 structural shock through structural break modelling or high-frequency panel data, and the reported correlations should therefore be interpreted with substantial caution. The third layer provides an approximate CO 2 emission estimation (RE3). Following the ICCT methodology [ 27], a factor of 90 g CO 2 per revenue passenger-kilometre (RPK) was applied. Average flight distance was estimated at 1500 km based on the geographic profile of BEG’s 2024 destination network. The network is dominated by intra-European routes (~500–2500 km), with long-haul services to North America and China accounting for less than 5% of annual departures. A weighted approximation (95% of passengers averaging ~1200 km; 5% averaging ~8500 km) yields a network mean of approximately 1565 km, supporting 1500 km as a conservative central estimate. A sensitivity analysis confirms that varying this assumption by ±300 km changes the CO 2 estimates by approximately ±20%. This estimate excludes non-CO 2 warming effects [ 6], such as contrail formation and NO x-induced ozone changes, which increase the effective climate impact of aviation by a factor of 2–3 relative to CO 2 alone, and should be interpreted as a conservative lower bound on the airport’s total climate footprint. The fourth layer examines the proportionality relationship between traffic growth and emission growth (RE3), applying the conceptual distinction between absolute decoupling (emissions decline while traffic grows) and relative decoupling (emissions grow more slowly than traffic) [ 32]. Given that the emission estimation in this study uses a fixed emission factor per passenger-kilometre, the analysis captures structural proportionality rather than marginal efficiency changes, a methodological limitation that is acknowledged in the limitations section. Figure 1 illustrates the research flowchart. 4. Results 4.1. Traffic Recovery and Operational Performance Recovery was gradual: 53.3% of the baseline in 2021, 91.1% in 2022. In 2023, passenger numbers exceeded the pre-pandemic level by 29%, marking a clear transition from recovery to growth. In 2024, a new passenger record of 8.37 million was set (+35.9% vs. 2019) [ 16]. Notably, passenger growth (+35.9%) outpaced aircraft movement growth (+22.4%), raising the passengers-per-movement ratio from 87.5 to 97.2, broadly consistent with RE4. The broader economic significance of aviation in Serbia is captured by IATA (2024) data [ 2]: the direct aviation sector employs approximately 6200 people and generates USD 160 million in output, while total contribution through supply chains, employee spending, and tourism reaches USD 1.3 billion and 45,500 jobs. Figure 2 illustrates the parallel recovery trajectories of passenger traffic at BEG and foreign tourist arrivals in Serbia, indexed to 2019 = 100, providing a visual summary of the co-movement. 4.2. Correlation Analysis Pearson correlation analysis between annual passenger traffic at BEG, foreign tourist arrivals in Serbia [ 38], and annual GDP growth rate [ 39] for 2019–2024 yielded the following results relevant to RE1 and RE2: (1) a positive co-movement between passenger traffic and foreign tourist arrivals (r = 0.970; p = 0.001), providing preliminary exploratory evidence consistent with RE1; however, this coefficient must be interpreted with caution as it is substantially inflated by the shared COVID-19 structural break of 2020–2021 and (2) no detectable association between passenger traffic and annual GDP growth rate (r = 0.143; p = 0.79), whereby no statistically detectable relationship could be identified in the available sample, consistent with RE2. The results are summarised in Table 2. This high coefficient is strongly influenced by the shared pandemic-related collapse and recovery pattern observed across all mobility-dependent variables during 2020–2021. The full annual data, including estimated CO 2 emissions, are presented in Table 3. 4.3. CO 2 Emission Proxy Estimation and Proportionality Assessment CO 2 emissions attributable to BEG passenger traffic were estimated using a factor of 90 g CO 2 per RPK [ 27] and an average flight distance of 1500 km. The annual estimates are presented in Table 4. Estimated emissions were 0.831 Mt in 2019 (baseline), fell to a minimum of 0.257 Mt in 2020, and recovered to 1.130 Mt in 2024, representing a net increase of 35.9% above the pre-pandemic baseline. 5. Discussion 5.1. Traffic Recovery in Comparative Perspective The growth in the passengers-per-movement ratio from 87.5 in 2019 to 97.2 in 2024 is broadly consistent with RE4 and is consistent with the recovery-phase behavior documented by Graham [ 4], whereby airlines deploy larger aircraft and achieve higher load factors as demand normalizes. However, as this indicator does not capture load factor, fleet structure, or ASK/RPK data, it should be treated as a proximate rather than definitive measure of operational efficiency improvement. The divergence between passenger (+35.9%) and cargo recovery (−14.2% vs. 2019 baseline) warrants specific attention. Button and Yuan [ 24] demonstrate that airfreight demand is driven by structurally different economic determinants than passenger demand, particularly high-value manufacturing exports, pharmaceutical supply chains, and just-in-time logistics, and responds to industrial output cycles rather than tourism or diaspora-driven passenger flows. For Serbia, whose export structure is undergoing transition toward higher value-added products, the slower cargo recovery may reflect both the specificity of cargo airline services at BEG and the composition of Serbian trade flows. 5.2. Aviation–Tourism Nexus and Economic Implications The absence of a detectable association between passenger traffic and annual GDP growth rate (r = 0.143; p = 0.79) does not allow for the detection of a detectable relationship in the available sample, a result consistent with RE2. Button and Taylor [ 22] demonstrate that aviation’s contribution to national economic output operates through indirect and long-lagged channels, foreign direct investment, export facilitation, productivity spillovers, and services trade growth, which are not captured by short-run annual GDP growth rates. Allroggen and Malina [ 23] further show that aviation’s economic effects are heterogeneous across airport types and traffic structures and tend to be larger and more detectable at airports with significant international hub functions and long-haul connectivity. The BEG trajectory, marked by rapid post-pandemic growth and expanding intercontinental reach, suggests these effects may become more readily measurable as the hub function matures, but a six-observation time series spanning an acute external shock is insufficient to detect them statistically. 5.3. Environmental Footprint The proportional relationship observed in the BEG case must be interpreted in light of the fixed emission factor methodology, which by construction produces a one-to-one emissions-to-traffic ratio. Actual emission intensity per RPK has declined gradually across the aviation sector as newer aircraft are deployed, suggesting that real-world emissions growth may be somewhat lower than estimated here. Fleet modernisation at Air Serbia during 2022–2024, including the replacement of ATR 72-200/500 with more fuel-efficient ATR 72-600 aircraft, suggests that actual emission intensity may have been somewhat lower than the fixed-factor estimate [ 37]. Nonetheless, even under more optimistic efficiency assumptions, a 20–25% improvement in fleet fuel efficiency, broadly consistent with ICCT estimates of fleet-wide improvements over 15 years, would still leave a net emissions increase of approximately 10–15% above the 2019 baseline by 2024. This aligns with the conclusion of Gössling and Humpe [ 31] that structural barriers to net-zero aviation are not addressable through efficiency improvements alone. Gössling and Humpe [ 31, 41] further demonstrate that aviation faces deep structural barriers to net-zero transition, including the cost gap of sustainable aviation fuels, limited alternative-technology readiness, and the absence of binding volume-constraining regulation. When non-CO 2 warming effects are incorporated following Lee et al. [ 7], primarily contrail formation and NO x-induced ozone changes, which increase effective radiative forcing by a factor of 2–3 relative to CO 2 alone, the indicative total climate footprint of BEG passenger traffic in 2024 is in the range of 2.3–3.4 million tonnes of CO 2-equivalent, underscoring the scale of the environmental challenge even for a mid-sized European hub. For Serbia, which is conducting EU accession negotiations that include the transposition of climate legislation, these findings provide exploratory, indicative evidence of near-term policy implications. The EU ETS free allocation to aircraft operators is being progressively reduced by 25% in 2024 and 50% in 2025, moving to full auctioning by 2026 [ 42]. Airlines operating on Serbian routes will face increasing CORSIA compliance obligations. Thomas and Scandurra [ 9] document that airport operators in EU member states are under growing regulatory pressure to integrate SDG reporting and adopt formal decarbonisation strategies, while Jia et al. [ 10] show that airports in candidate and accession countries tend to lag larger hub airports in SDG alignment, partly due to lower regulatory pressure and limited access to green infrastructure financing. For BEG, the current absence of domestic SAF production capacity and the limited national regulatory framework for aviation decarbonisation represent structural vulnerabilities that require proactive policy attention before EU accession obligations come into full effect. 5.4. Sustainability Perspective: SDG Framework Aviation growth in Serbia is here assessed in exploratory terms against the 2030 Agenda [ 6] and the relevant SDGs using an interpretative framework rather than formal SDG indicators or assessment thresholds, revealing both alignment and structural tension. The three SDGs most directly implicated, SDG 8, SDG 9, and SDG 13, are not fully compatible in the short-to-medium term in the context of rapid aviation growth, which is the central sustainability trade-off this study documents. SDG 8 (Decent Work and Economic Growth)—The direct aviation sector employs approximately 6200 workers and generates USD 160 million in output, with broader effects reaching USD 1.3 billion and 45,500 jobs [ 2]. The co-movement between passenger traffic and tourist arrivals (r = 0.97, noting the COVID-19 structural break caveat) is broadly consistent with additional employment multiplication in hospitality, retail, and transport services, supporting the argument that aviation’s connectivity function is a material contributor to SDG 8 targets in Serbia. SDG 13 (Climate Action)—The 35.9% growth in estimated CO 2 emissions, based on a conservative lower-bound approximation excluding non-CO 2 effects that may increase the effective climate impact by a factor of 2–3, represents a clear tension with climate commitments. The EU’s progressive tightening of ETS obligations for aviation [ 42], combined with ICAO’s net-zero 2050 target, creates an increasingly binding regulatory environment for airlines operating at BEG. Thomas and Scandurra [ 9] and Jia et al. [ 10] demonstrate that airport operators are under growing regulatory pressure to align operations with SDG frameworks, yet compliance remains uneven, particularly in non-EU and candidate countries. The tension between SDG 8, SDG 9, and SDG 13 makes aviation a paradigmatic case of sustainable development trade-offs: a sector that supports economic growth and social inclusion simultaneously generates environmental costs that policymakers cannot defer. Long-term sustainability requires an active decarbonisation policy [ 6, 8] combining EU ETS compliance, CORSIA obligations, SAF incentives, and infrastructure greening, implemented concurrently with connectivity growth, not sequentially. Long-term projections of global mobility demand, driven by rising incomes and a structural shift toward faster modes of transport, indicate that demand pressures on aviation will intensify well beyond the current study period [ 43], further reinforcing the urgency of proactive decarbonisation policy as a structural rather than transitional imperative. 5.5. Limitations Several limitations of this study must be acknowledged. First, the small sample of n = 6 annual observations substantially reduces statistical power, making correlation results sensitive to individual data points and structural breaks; findings should be treated as indicative rather than conclusive. The study, therefore, does not seek to provide formal inferential hypothesis testing, but rather an exploratory descriptive assessment of temporal co-movement patterns during the observed recovery period. The COVID-19 structural break of 2020 is particularly problematic because it generates mechanical co-movement between all variables that contracted and recovered together, which inflates Pearson coefficients regardless of any underlying relationship. Future studies with longer time series, ideally 15+ years, and appropriate structural break tests would substantially strengthen causal inference. Given the exploratory case-study design and limited annual dataset, the study does not attempt multivariate econometric modelling capable of isolating endogeneity, confounding effects, or causal dynamics among aviation, tourism, and macroeconomic variables. Second, the CO 2 estimation is approximate: it relies on a global average emission factor [ 27] and an estimated average flight distance and excludes non-CO 2 warming effects [ 7] that could increase the effective climate footprint by a factor of 2–3. Additionally, the fixed-factor approach does not capture year-on-year fleet efficiency improvements, meaning the absolute emission estimates are likely conservative and the apparent absence of relative decoupling is partly a methodological artefact. A more precise estimation would require route-level data and airline-specific emission factors. Third, the study covers only one airport and one country; comparative analysis with regional airports, Ljubljana (LJU), Sarajevo (SJJ), Skopje (SKP), and Sofia (SOF), would contextualise the findings and improve their transferability. Fourth, the reliance on aggregate official data precludes analysis of passenger composition, transfer traffic share, seasonality, and route-level economics, all of which are relevant to a complete assessment of aviation’s economic and environmental impacts. Finally, the theoretical framework adopted in this study, catalytic effects combined with the sustainability trade-off perspective, provides a useful but partial lens. Frameworks that more directly model the structural conditions for decarbonisation, such as the Technology Acceptance Model applied to SAF adoption or transition management frameworks, could complement the approach used here. Future research should address these limitations by employing panel data covering multiple airports and longer time series, applying econometric identification strategies [ 19, 20], and integrating more granular environmental modelling that accounts for non-CO 2 effects. 6. Conclusions Belgrade Nikola Tesla Airport’s trajectory from pandemic collapse to a record 8.37 million passengers in 2024 encapsulates a dynamic that extends well beyond a single infrastructure node. This exploratory study has shown that rapid air traffic recovery in a small transition economy is simultaneously an economic asset and an environmental liability, and that the two cannot be governed in isolation. Passenger traffic at BEG grew by 35.9% between 2019 and 2024, reaching a record 8.37 million passengers, while the destination network expanded from 99 to 135 routes. The passengers-per-movement ratio improved from 87.5 to 97.2, indicating a proximate improvement in operational efficiency. A descriptive co-movement was observed between passenger traffic and foreign tourist arrivals (r = 0.970), consistent with the catalytic effects framework, while no detectable association was found between passenger traffic and GDP growth rate (r = 0.143). Estimated CO 2 emissions grew proportionally from 0.831 to 1.130 million tonnes, reflecting the fixed-factor methodology applied. Preliminary evidence suggests that aviation growth at BEG appears to be associated with Serbia’s inbound tourism expansion, with the widening of the destination network providing a plausible mechanism for the co-movement observed between passenger traffic and foreign tourist arrivals. These findings reflect associations and do not establish causal relationships. At the same time, the absence of detectable association between short-run GDP growth and air traffic is consistent with the established literature suggesting that aviation’s economic contribution operates through indirect and long-lagged channels, a finding that cautions against simple connectivity-to-growth narratives in policy discourse. Most critically, the fixed-factor estimation framework applied in this study produced proportional growth between estimated CO 2 emissions and passenger traffic. This result should be interpreted as an illustrative outcome of the methodological assumptions applied rather than as independent empirical proof of the absence of absolute decoupling. What this study contributes is not merely empirical documentation of these patterns in an underrepresented context, but a demonstration that the catalytic effects framework and the sustainability trade-off perspective must be applied jointly to capture the full picture of aviation development in EU candidate economies. These findings are specific to BEG, and their transferability to other contexts requires further investigation. The proportional emission growth observed under the current methodology suggests that, in the absence of an active decarbonisation policy, the tension between aviation expansion and climate obligations is unlikely to resolve itself as the market matures. The exploratory findings of this study are consistent with the policy implication that realising the economic potential of BEG’s expanding hub function while meeting the climate obligations embedded in Serbia’s accession trajectory requires simultaneous action, not sequential prioritisation. While the single-airport design limits the generalisability of these conclusions, structural responses including regulatory alignment with EU ETS and CORSIA, investment in SAF readiness, and integrated sustainability reporting appear to be preconditions for durable competitiveness rather than post-growth luxuries. As an exploratory study, the contributions of this paper are necessarily modest in scope. Nonetheless, three descriptive contributions can be identified. First, the study provides preliminary empirical evidence on post-pandemic air traffic recovery in a small transition economy, applying a theoretically grounded exploratory descriptive framework to a context that remains underrepresented in the aviation economics literature. Second, it demonstrates the co-existence of descriptive associations consistent with catalytic economic effects (supporting SDG 8 and SDG 9) and structural emissions growth (in tension with SDG 13) within a single case, making BEG a concrete illustration of the aviation sustainability trade-off. Third, by introducing the proportionality assessment concept explicitly into the analysis and linking it to Serbia’s EU accession obligations, the study contributes to the policy-relevant literature on sustainability transitions in candidate countries. These contributions are of a preliminary and descriptive nature; more definitive conclusions would require longer time series, panel data, and more granular environmental modelling. Author Contributions Conceptualization, S.K. and M.S.; methodology, M.Z.; software, T.G.; validation, T.G., N.C. and M.Z.; formal analysis, J.J.; investigation, P.D.; resources, M.S.; data curation, M.Z.; writing—original draft preparation, S.K. and V.R.; writing—review and editing, N.C. and N.B.; visualization, P.D.; supervision, J.J.; project administration, D.J. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement Ethical review and approval were waived for this study due to national legislation. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study may be obtained on request from the corresponding author. Conflicts of Interest The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: BEG Belgrade Nikola Tesla Airport (IATA: BEG; ICAO: LYBE) CO 2Carbon Dioxide CORSIA Carbon Offsetting and Reduction Scheme for International Aviation EU ETS European Union Emissions Trading System GDP Gross Domestic Product GHG Greenhouse Gas IATA International Air Transport Association ICAO International Civil Aviation Organization ICCT International Council on Clean Transportation LCC Low-cost carrier OLS Ordinary Least Squares RPK Revenue Passenger-Kilometre RZS Statistical Office of the Republic of Serbia (Republički zavod za statistiku) SAF Sustainable Aviation Fuel SDG Sustainable Development Goal ACI Europe; SEO Amsterdam Economics. The Economic and Social Impact of European Airports and Air Connectivity; ACI Europe: Brussels, Belgium, 2024. [ Google Scholar] IATA. 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Year Passengers Aircraft Movements Cargo & Mail (kg) Pax/Movement Recovery Index (2019 = 100) 2019 6,158,897 70,365 21,428,158 87.5 100.0 2020 1,903,540 33,622 17,753,931 56.6 30.9 2021 3,285,760 48,842 14,736,198 67.3 53.3 2022 5,610,487 65,644 13,548,848 85.5 91.1 2023 7,946,784 83,310 15,234,253 95.4 129.0 2024 8,367,931 86,122 18,385,256 97.2 135.9 Table 2. Passenger traffic at BEG, foreign tourist arrivals in Serbia, and GDP, 2019–2024. Table 2. Passenger traffic at BEG, foreign tourist arrivals in Serbia, and GDP, 2019–2024. Variable Pair n r r 2p-Value BEG passenger traffic—Foreign tourist arrivals 6 0.970 0.94 0.001 BEG passenger traffic—GDP growth rate 6 0.143 0.02 0.79 Two-tailed Pearson correlation coefficients reported as descriptive indicators of co-movement; p-values should not be interpreted as evidence of statistical significance given n = 6 and the COVID-19 structural break; r 2 = coefficient of determination. Table 3. Parallel evolution of key variables at BEG and in Serbia, 2019–2024. Table 3. Parallel evolution of key variables at BEG and in Serbia, 2019–2024. Year Pax BEG (mn) Index (2019 = 100) Foreign Tourists (mn) Index (2019 = 100) GDP Growth (%) CO 2 Est. (mn t) 2019 6.16 100.0 1.85 100.0 4.75 0.831 2020 1.90 30.9 0.45 24.3 −0.95 0.257 2021 3.29 53.3 0.87 47.0 7.95 0.444 2022 5.61 91.1 1.97 106.5 2.63 0.757 2023 7.95 129.0 2.12 114.6 2.50 1.073 2024 8.37 135.9 2.38 128.6 3.80 1.130 Table 4. Estimated CO 2 emissions and proportionality assessment, Belgrade Nikola Tesla Airport, 2019–2024. Table 4. Estimated CO 2 emissions and proportionality assessment, Belgrade Nikola Tesla Airport, 2019–2024. Year Passengers (mn) CO 2 Emissions (mn t) Change vs. 2019 (%) Proportionality Assessment 2019 6.16 0.831 — Baseline 2020 1.90 0.257 −69.1 N/A (shock) 2021 3.29 0.444 −46.7 N/A (recovery) 2022 5.61 0.757 −8.9 N/A (recovery) 2023 7.95 1.073 +29.1 Proportional growth under fixed-factor estimation 2024 8.37 1.130 +35.9 Proportional growth under fixed-factor estimation Note: Estimation based on [ 27]; excludes non-CO 2 radiative forcing effects [ 7]. Values represent a conservative lower bound. Proportionality assessment compares 2023–2024 growth-phase observations against the 2019 baseline. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Air Traffic Growth and Sustainability Trade-Offs: An Exploratory Study of Belgrade Nikola Tesla Airport, Serbia
