Using weekly data from November 25, 2019, to August 30, 2024, this study examines how potential physical and transitional climate risks affect Qatar's MSCI index and sectoral markets. The global market data are sourced from the MSCI index, available through FactSet, and sector-specific composite market data, which include the Banks, Construction & Materials, Financials, Telecom, Healthcare, Industrial, Energy, Utilities, and Materials indices, are available from Bloomberg. These indices provide a comprehensive overview of Qatar's sectoral system capabilities in response to climate risks.

This study employs textual analysis to quantitatively assess both transitional climate risk (TCI) and physical climate risk (PCI), following the taxonomy developed in the work of [11], which includes Google Trends [48,49]. TCI is assessed in terms of a Google Trends index based on textual information from search terms related to environmental and energy transitions (defined as "Environmental," "Renewable energy," "ESG," "Hydrogen," "Transition," "Storage," "Solar," "Oil prices," "Gas," "Fossil fuel," "Carbon dioxide," and "Emissions"). PCI is similarly derived from Google Trends indices for natural disasters and other environmental hazards, including Floods, Temperatures, Pollution, Weather, Disasters, Warming, Environmental Issues, Ecosystems, Coastal Areas, Forests, Soils, Carbon, and Dust Storms. After comparing their proportional share within Google Trends data, the specific selection of words is weighted. Such an analysis ensures that terms have as much relevance as possible and will accurately represent public concern and climate risk discourse. To mitigate concerns over the measurement validity of our climate risk indices (PCI & TCI). However, Google Trends is often used in academic research papers as an essential proxy indicator of real-time public awareness and concern related to climate issues [[50], [51], [52], [53]]. In the robustness test, we conducted additional tests to substitute the PCI and TCI with the surface temperature (TMP) in Qatar, which was sourced from the Qatar Meteorology Department.

In the estimation process, we employ mediator variables represented by the OVX (Oil Volatility Index) data from the Federal Reserve Bank of St. Louis. Oil market uncertainty, a key driver of Qatar's economy, which relies heavily on hydrocarbon exports, is reflected in the OVX. By blending market indices from the global landscape with sectoral data and advanced measures of climate risk derived from textual analysis, this study demonstrates how to effectively capture the interactions between climate risk and financial markets in Qatar, an emerging financial market.

The selected sample period, from 2019 to 2024, is adequate based on the limited availability of high-quality, high-frequency financial and textual climate data, which has only become available over the past several years, particularly for emerging markets, including Qatar. This period encompasses large-scale global disturbances, such as the spread of COVID-19, the Ukraine-Russia conflict, and the Red Sea tension, as well as increased discourse related to climate-related financial policy, all of which are crucial for understanding how financial market sensitivity is shaped by exposure to physical and transitional risks. As of February 2025, the market capitalization of the Qatar Stock Exchange (QSE) totaled USD 168.52 billion, and it stood its ground among its regional peers. By comparison, the DFM in Dubai was valued at around USD 180 billion, while the Muscat Stock Exchange was significantly smaller, with orders of magnitude, at approximately USD 24 billion. Although Qatar's financial market is relatively small, its integration with climate-sensitive sectors is high, and sectoral and volatility-related data are regularly observed at daily frequencies (which is not often the case for the Omani datasets). Dubai's DFM has a slight advantage over Qatar in terms of market capitalization; however, the DFM's sector coverage and climate-related financial disclosure are less comprehensive, which may impact quantile-based results. Thus, the QSE is robust in approach and meaningful in its economic implications in investigating the sectoral climate financial impacts in a hydrocarbon economy, which provides a methodological convergence and economic relevance behind the empirical facts of the 2019–2024 transmission period and the regional positioning specification. Cold seasons are short and nonexistent, and the temperature in any season—especially at night— can hover around 37 to 46 degrees Celsius. Still, the Qatar climate is consistent, with hot summers, when the temperature may reach the 40-degree mark from June to August, mild midseasons from March to May and September to November. This is generally in line with other countries in the Gulf, such as the Kingdom of Saudi Arabia, the United Arab Emirates (UAE), and Oman, which have similar seasonal patterns and temperature magnitudes due to their shared desert landscapes and coastal proximity.

The m-QQR model is employed in this study to analyze the impact of physical and transitional climate risks on the MSCI index and sectoral markets in Qatar. In particular, the analysis considers OVX a moderating variable, as it plays a vital role in the structure relating climate risks to the financial market performance of an oil-dependent economy. The integration of OVX into the model enhances its ability to account for the dynamics between climate risks and oil market volatility, allowing for a more nuanced perspective on how these forces converge to influence market trends.

The QQR method, hailed by [54], has since captured the attention of researchers for its dependable feature of tracing the quantile-dependent effect of quantile-independent variables on the quantile-dependent responses' parameters through different market conditions [37,55]. This technique is beneficial in analyzing non-linear relationships that cannot be sufficiently modeled with standard linear regression approaches [56,57]. The QQR has also been applied to investigate the tail behavior of distributional dynamics, which explore heterogeneity in data across several fields, most notably in financial and climate risk assessments [36,58,59].

Extending the groundwork of [54], an advanced m-QQR (i.e., Multivariate Quantile-on-Quantile Regression method) was developed by [35]. It is an improvement over earlier approaches, such as QQR, as it does not suffer from some of the potential biases of omitted variable effects due to its ability to integrate moderating variables and relationship terms within the regression framework. The m-QQR approach provides a more comprehensive examination of interactions across multiple variables, considering whether external influences act as moderating variables. This level of detail in the data significantly enhances the robustness and interpretability of results from such studies, which typically involve interrelated economic, financial, and environmental factors.

The m-QQR and QQR models are especially well-suited for addressing analytical problems such as heteroskedasticity, skewness, serial correlation, and structural breaks [54]. Such challenges commonly arise in datasets related to market fluctuations, climate-related risks, and sectoral analyses. Moreover, these techniques enable researchers to evaluate symmetry effects in variable relationships, providing critical insights into the differential effects of one variable on another across upper and lower quantiles. We can write the equations models respectively as:

Equity market data using MSCI indices is appropriate. It is the most widely used equity benchmark for assessing the impact of climate risk on financial markets across various geographies. The MSCI is widely used in evaluating ESG market performance and modeling climate-related hedging strategies, as highlighted by [[60], [61], [62]]. To be more specific, we employ the sector-specific composite Qatar market data, which include the Banks, Construction & Materials, Financials, Telecom, Healthcare, Industrial, Energy, Utilities, and Materials sectoral markets; ϵ and θ denote the error component and quantile, respectively. We utilize the PCI and TCI to assess climate risk, drawing on the literature [10,63].

Where:

: index at time t

: index at time t-1

: index returns at the time.

Fig. 1 highlights the return series in this study, including Physical Climate Risk (PCI), Transitional Climate Risk (TCI), Oil Volatility Index (OVX), MSCI index, and sectoral markets in Qatar, as well as Banks, Materials, Financials, Coal, Energy, Healthcare, Industrial, Telecom, and Utilities sectoral indices. PCI and TCI exhibit high-level volatility and spikes during high-profile incidents, such as the COVID-19 pandemic, the Russia-Ukraine conflict, Red Sea tensions, and other climate-related events, indicating their high sensitivity to external shocks. The well-known Volatility Index for oil, OVX, exhibits classic upward spikes during periods of oil market tension, notably in 2020, serving as a gauge of energy market volatility. However, high-frequency indicators such as the GPR index are currently unavailable for Qatar, which is only available  for 44 advanced countries in US media sources [64]. However, OVX only partially captures global geopolitical shocks, including the conflict between Russia and Ukraine and the Red Sea conflict, which affect the energy markets and financial sectors of oil-exporting economies. Other indices, such as Energy and Financials, exhibit higher volatilities, reflecting their vulnerability to economic shocks and fluctuations in oil prices, in contrast to Healthcare and Utilities, where stability is a reflection of robustness. Returns for the global Qatar MSCI index appear relatively stable compared to the sectoral indices.

At mid-quantiles of PCI and bank performance, the effect ranges from neutral to mildly positive values, indicated by the green areas. This suggests a balanced effect where the sector adjusts to moderate levels of physical risk from climate change potential. At the same time, OVX probably alleviates extreme uncertainty within the sector, leading to sector stability. For TCI, the dynamic follows a similar effect, with a negative impact at lower quantiles and a neutral impact at medium quantiles, before the association becomes positive (up to 0.4) at the upper quantiles of TCI and Bank performance, as illustrated in the yellow regions. As a mediator, OVX appears to be associated with variation in the effect of climate risk, dampening their relationship with sectoral performance. One potential positive for the Banks sector is that at low climate risk levels, OVX may reduce uncertainty, thus stabilizing the market and magnifying the positive effects on the Banks sector. At high climate risk levels, OVX increases uncertainties and may hamper the sector's opportunities to benefit from climate risk.

Regarding the materials sector, as shown in Fig. 3, Fig. 3, the association is strongly negative for PCI at the lowest PCI and Materials performance quantiles, dropping to nearly −1. Low PCI levels coincide with negative returns, reflecting potential challenges faced by the sector under TCI and weak performances. Across the medium quantiles of PCI and Materials performance, the effect is neutral to mildly positive, appearing in the green-colored range of 0 to 0.5. It denotes the adjustment time frame during which materials have begun to cope with physical risks, bolstered by the stabilizing role of OVX in easing oil price volatility. At high levels of the quantiles of PCI and the Materials sector, the effect turns significantly positive and peaks at around 2, implying that high risks to physicality create strong growth in Materials, possibly owing to increased demand for climate-resilient materials, while amplifying a positive impact due to OVX stabilization of uncertainties in the oil market. The dynamics for TCI are similar but less pronounced, explaining how, at the bottom quantiles of both TCI and Materials performance, the effect is negative, with values close to −0.2. Weak market conditions, accompanied by low transitional risks, appear to add extra pressure on the sector, possibly due to inefficiencies or costs incurred during the early stages of implementing climate policy. Including the medium quantiles, the effect becomes neutral, as evident between approximately zero and 0.2, shedding light on the sector's ability to respond to TCI. At the upper quantile, this association increases, peaking at around 0.4 (yellow regions). These results are consistent with climate transition objectives, which are that high transitional risk exacerbates the growth of demand for sustainable materials and that OVX mitigates uncertainty in oil markets, as both processes contribute to this growth. These findings would also underscore the need to manage oil price volatility, which can erode the resilience and ability of materials sector companies to capitalize on both physical and transitional climate opportunities.

Fig. 3, Fig. 3 show that for both PCI and financial sector performance, there is a negative effect on PCI. It indicates that in times of weak financial market performance and low credible physical climate risks, PCI worsens operational and market weaknesses, further reinforced by OVX-induced oil price volatility. At the medium quantiles, the effect stabilizes and is neutral to slightly positive (0 to 0.5). Such resilience signifies how the financial sector has already adjusted for physical risks, powered by OVX, the commodity-driven measure that reduces oil market volatilities. At both ends, as the performance of the PCI and Financials sector becomes high (in the upper quantiles), the association becomes strong and positive, reaching its peak around 1, indicating that financial institutions capitalize on high physical risks to pursue growth opportunities. TCI exhibits a similar pattern, albeit at a lower magnitude. There is a negative impact at lower TCI and Financials quantiles, with values between −0.2 and −0.3, facing initiatory costs associated with early-stage climate policies and ESG compliance pressures under low transitional risks and weak conditions, as well as oil uncertainty. In the medium quantiles, however, the impact shifts from neutral to slightly positive, approximately 0.2, as the sector gradually adjusts to transitional risks with the help of OVX in reducing volatility. The peak of 0.4, indicated by the yellow regions, indicates that the effect becomes positive when both TCI and Financials sector performance are at high quantiles, near the upper bound. Thus, it highlights the need for transitional risks to be embraced within the Finance and ESG sectors.

The results reveal that lower PCI and the Energy sector performance quantiles show negative effects, as evidenced by the blue regions (approximately −0.5) in Fig. 2.7. The effect size at medium quantile levels shifts from neutral to positive, as evidenced by the difference between 0 and 0.5. The influence is significantly positive at upper quantiles, when both PCI and Energy sector performance are high, peaking at 2.0, as denoted by the yellow areas. This highlights the sector's capacity to benefit from heightened demand for energy-related infrastructure and investments under elevated physical risks, underpinned by the stabilization of oil market dynamics, as evidenced by OVX. For TCI, the patterns are the same but less pronounced. Outside the lower quantiles of TCI and Energy performance, where the impact is negative, the exact signatures are upwards of −0.5 to −0.7, highlighted in blue. In Fig. 2.8, the decreasing transitional risk associated with low energy sector performance creates extra tension, most notably through the front-loaded costs of climate policies in early-stage implementation, exacerbated by OVX-fueled uncertainty. The impact is neutral to slightly negative (−0.2 to 0) at medium quantiles. At higher quantiles, where performance in TCI and Energy is increased, the influence becomes slightly positive and peaks at 0.2. This indicates the sector's capacity to capitalize on transition risks, such as investment in renewable sources, with OVX acting as a stabilizing factor by alleviating oil market uncertainty.

Fig. 3, Fig. 3 depict the effect of PCI and TCI on the Healthcare sector. Regarding PCI, our findings show that during periods of minimal PCI and Healthcare returns, the impact was intensely deleterious, with a nearly −4 result, indicating that low PCI and weak performance introduced considerable difficulties, such as operational disruptions and heightened vulnerabilities, which were exacerbated by OVX-driven oil market volatility. At upper quantiles where PCI and Healthcare sector execution were robust, the association became neutral to somewhat positive, peaking near 0.5. For TCI, the outcomes evidenced less severe negative impacts compared to PCI. The influence was deleterious at minimum quantiles of both TCI and Healthcare sector performance, ranging from −0.1 to −0.3, as represented by the blue regions. This suggests that weak market conditions and low transitional risks presented additional difficulties, such as policy-related expenses, which were amplified by OVX-induced market instability. At median quantiles, the impact became neutral to somewhat positive, ranging from 0 to 0.2, as indicated by the green regions. This reflected the healthcare sector's growing capacity to adapt to transitional risks and leverage sustainability opportunities. The relationship was positive at upper quantiles, where both TCI and Healthcare are high, peaking at around 0.6, as indicated by the yellow regions. This highlighted the sector's ability to capitalize on transitional risks through investments in sustainable technologies, as well as Qatar's excellent performance in the healthcare sector.

Fig. 3, Fig. 3 illustrate the effects of PCI and TCI on the industrial sector. The effect is negative at lower PCI and Industrial sector performance quantiles, indicating weak sector performance and lower physical climate risks. PCI increases vulnerabilities, most of which arise from operational disruptions and supply chain disruptions. At medium quantiles, the effect becomes neutral to slightly negative (−1 to 0), as illustrated by the green and yellow areas, indicating a gradual adaptation of the industrial sector to moderate physical risks. However, at the upper quantiles where both PCI and Industrial sector performance are high, the effect transitions to a slight positive, with a peak near 0.5, as displayed in the yellow areas. For TCI, the dynamics are revealed more pronouncedly in the analysis. This is especially true at the lower quantiles of both TCI and Industrial sector performance, where the effect is strongly negative, indicating that the industrial sector is the most affected by the TCI in Qatar. At the upper quantiles, where both TCI and Industrial sector performance are high, the effect flips to strongly positive.

Concerning the effect of PCI and TCI on Telecom, as displayed in Fig. 3, Fig. 3, it shows that the effect is highly negative, about −4 at low PCI and Telecom performance quantiles, indicating that during periods of weak sector performance and low-level physical risks, PCI poses an enormous threat, driven by increasing uncertainty in the oil market due to OVX. Impacts are mostly neutral to mildly positive, ranging from 0 to 0.5, with medium quantiles, as indicated by the green and yellow areas. At the upper quantiles, where both PCI and Telecom performance are high, the associated coverage has a strong positive impact, reaching a peak of around 1. For TCI, the impact is more muted. This is somewhat negative (between −0.5 and −0.7 in the blue regions of TCI and Telecom performance) at the lower quantiles of both TCI and Telecom performance, leading to moderate challenges. The impact becomes neutral or positive for medium quantiles, between 0 and 0.2, as illustrated by the green regions. This association becomes positive at the upper quantiles, where TCI and telecom performance are strongest, and peaks at around 0.3.

Fig. 3, Fig. 3 illustrate the impact of PCI and TCI on the financial utility index. The influence is negative at lower PCI and Utility performance quantiles, dipping as low as −0.3. This suggests that during weak markets and low physical dangers, the sector faces hurdles such as vulnerable infrastructure and heightened operating expenses, exacerbated by uncertainty from fluctuating oil prices. The impact is neutral and somewhat positive at middle quantiles, ranging from 0 to 0.2. At upper quantiles, where PCI and Utilities results are high, the association becomes strong, peaking near 0.6, shown by the yellow regions. For TCI, findings reveal a similar pattern, with stronger negative effects at lower quantiles. The influence is negative at the lower TCI and Utilities performance quantiles, ranging from −0.3 to −0.6. At middle quantiles, the impact steadies to neutral or somewhat positive, ranging between 0 and 0.2, indicating the industry's ability to adapt to moderate transitional dangers, assisted by OVX's moderating impact. At upper quantiles, where TCI and Utilities results are strong, the influence becomes positive, peaking near 0.5.

Lastly, Fig. 3, Fig. 3 provide the effect of PCI and TCI on the MSCI index (financial market). For TCI, results at the lower quantiles of TCI and MSCI indicate a negative impact from transitional risk and oil market uncertainty pressures during weak markets. At medium quantile levels, the effect is neutral, suggesting that the sector reacts strategically to transitional risks following oil price volatility. The association is positive at the upper quantile, with a peak around 0.4, indicating that good market conditions allow the MSCI index to mitigate transitional risks. For PCI, the results reveal significant negative effects at lower quantiles when physical climate risks and oil market volatility interact to exert additional downward pressure on MSCI returns (up to −1.5). Under market conditions similar to those that signal physical market trends, such as strong fundamentals, PCI has a significant positive effect at higher quantiles, reaching a maximum value of around 2, consistent with the MSCI index's ability to capitalize on physical risks despite the mediation of OVX.

Our study sheds light on and advances the understanding of the m-quantile-dependent impacts of climate risks on financial markets, with implications for Qatar's sustainability transition. As [42] concludes in their study of the effect of climate risks on financial markets, we find that TCI encourages adaptive sectors such as Utilities, Telecoms, and Industrial during good times, while Energy and Healthcare suffer intensely under weak performance. This asymmetry highlights how specific sectors align with low-carbon transition pathways, underscoring their importance in Qatar's long-term sustainable development strategy.

Consistent with findings from [44], which suggest that temperature variability amplifies stock risks, our analysis reveals that physical climate risks exert downward pressure on indices at lower quantiles, indicating heightened vulnerability during downturns. Supporting Zhang et al. [33]., Documenting the disruptive effects of climate concerns on integration, especially in traditional energy economies, PCI harms markets most when they are already weak. However, in the financial and industrial sectors, PCI generates positive returns during stronger periods, echoing Li et al. [47], which shows that clean energy hedges fossil fuel volatility amid rising risks. These dynamics reflect transitional features outlined in sustainability frameworks, where resilience and diversification are central to navigating climate uncertainty. As Hu and Borjigin [46] found, geopolitical uncertainty and economic unpredictability increased volatility in green transitions. Oil market volatility worsened the impacts of PCI and TCI during downturns, but stabilizing resilience during upswings aligns. Gong et al. [37]observed that political and economic unpredictability exacerbates volatility for high-exposure companies, especially those sensitive to shifts. OVX amplifies climate dangers most during times of poor performance. In line with Bouri et al. [67], who demonstrate that climate risks systematically influence green-brown stock return gaps, favoring green stocks under strong policies or market actions, we observe that TCI and PCI have a positive impact on Utilities and MSCI during upswings, as these capitalize on changes in normal market conditions. Finally, the funding confirms the studies conducted by [46,47], which emphasize that integrating climate considerations into investment enhances efficiency and predictability. Our study highlights that sectors resilient to risks, such as Utilities and Telecom, offer stable returns, as green, adaptive investments serve as effective safe havens during periods of volatility. These industries appear to be well-positioned to capitalize on structural changes toward sustainable development if they operate in line with the cornerstones of sustainability transition theory, such as resilience, systemic innovation, and intersectoral integration. Our results contribute to theory by providing empirical evidence of how market resilience, sectoral diversity, and adaptive capacity serve as key exogenous drivers in promoting sustained socioeconomic shifts. Within the framework of Qatar Vision 2030, which emphasizes economic diversification, environmental sustainability, and human development, the above sector dynamics depict changing financial architectures that enable inclusive and climate-resilient growth. Additionally, the standout performance of Utilities and Telecoms under a growing climate risk backdrop appears consistent with the UN SDGs (notably Goals 7: Affordable and Clean Energy, 9: Industry, Innovation, and Infrastructure, and 13: Climate Action). These results support the multi-level perspective on transitions, according to which niche innovations and responsive actions in the market progressively impact and transform dominant regimes. Therefore, the observed market dynamics have practical implications for directing capital allocation and investment towards sustainability-congruent industries and for Qatar, engaging in a more comprehensive transition to a low-carbon, knowledge-based economy. The findings are country-specific to Qatar's market structure and economy; they are consistent with international studies of other hydrocarbon-based economies. Studies on Saudi Arabia and the UAE, for example, [68] have also revealed that energy-intensive industries are more sensitive to climate policy uncertainty and oil price risk, particularly in bearish market scenarios. This pattern is reinforced by evidence from Russia and Nigeria, which have recognized the financial and utility sectors as relatively more resilient to climate and geopolitical disruptions [69]. However, Qatar's concentrated and highly globalized financial market, as well as its ambitious national vision for diversification, could accelerate transition alignment more quickly than in larger, more reform-averse hydrocarbon economies. These cross-country regularities thus suggest that, while the direction and extent of sectoral climate sensitivity differ, a shared resilience architecture is possibly emerging in many critical sectors across resource-driven economies.

For both the TCI and the PCI, Fig. 4, Fig. 4 provide insights into how climate factors affect the materials sector's performance under different market conditions. At lower levels of index score and sector performance, the effects are predominantly negative, with transitional risks posing challenges from −0.4 to −0.1 and physical risks amplifying difficulties down to −0.8. However, with moderate index scores and performance, impacts stabilize around neutral to slightly favorable, ranging from 0 to 0.2 for transitional and from −0.1 to 0 for physical, indicating the sector's developing ability to adapt. At the upper quantile, where opportunities abound, the effects flip decidedly optimistic, with transitional risks maximized at 0.6 and physical risks leveraged to 0.3 gains. These findings, aligning with m-QQR analyses, demonstrate that the materials industry can both benefit from and be burdened by climate dynamics, with outcomes contingent on underlying market circumstances.

Fig. 4, Fig. 4 present the effects of PCI and TCI on the financial index. In the case of PCI, QQR presents a negative effect at the lower quantiles, −0.3 approximately, similar to m-QQR, but with larger effects due to OVX not being included as a mediator. At intermediate quantiles, the effect levels are somewhat positive, ranging from zero to 0.2, reflecting and incorporating the financial sector's adaptation to moderate physical risks. The positive effect of PCI remains robust at the upper quantile, with an upper bound of 0.7, validating the m-QQR model, which shows that the sector can enjoy favorable circumstances during high physical risks. In the case of TCI, QQR reveals adverse impacts at lower quantiles, ranging from around −0.2 to −0.3, but less intense than when using m-QQR, where OVX exacerbates the difficulties. For the medium quantiles, the impact stabilizes from neutral to slightly positive (0 to 0.2), which is consistent with m-QQR, indicating the sector's ability to cope with transition-related risks. For instance, m-QQR and TCI have a positive effect on upper quantiles, with a peak value of 0.3.

Turning to the effect of PCI and TCI on the energy sector, Fig. 4, Fig. 4 show that for TCI, the effect at lower quantiles of both TCI and Energy performance is negative, ranging from −0.2 to −0.3, as per the m-QQR findings; this is slightly less pronounced using m-QQR. The effect stabilizes to neutral or slightly positive at medium quantiles, ranging from 0 to 0.2, as indicated by the m-QQR results. It reflects the sector's ability to adapt to moderate transitional risks. TCI has a distinctly positive effect at the upper quantiles, peaking at 0.8. This suggests that the energy sector can effectively manage transitional risks under favorable market conditions. The figure is the same in both QQR and m-QQR. For PCI, QQR results mirror the earlier m-QQR findings. At lower PCI and Energy performance quantiles, the negative impact amounts to around −0.3, which is somewhat weaker than in m-QQR, with OVX amplifying the challenges from physical risks during weak conditions. The effect is flat to slightly negative at medium quantiles, ranging from −0.1 to 0, as in m-QQR. This is the sector's slow inward adjustment under moderate physical risks. PCI contributes well at the upper quantiles and stops at 0.4, confirming the m-QQR findings.

Regarding the effect of climate risks on healthcare, as shown in Fig. 4, Fig. 4, the TCI has negative effects for lower quantiles, neutral to slightly positive effects for medium quantiles, and positive effects for higher quantiles, following the m-QQR results. For PCI, the results follow a similar pattern to m-QQR, showing strong negative effects at lower quantiles, stabilization at medium quantiles, and positive effects at upper quantiles. For PCI, the impact at lower quantiles is negative, with a magnitude of around −0.6, showing the sector's higher exposure to physical risks when the performance parameter is lower. For medium quantiles, effects stabilized at neutral to slightly positive (between 0 and 0.2), indicating adaptation to moderate physical risks. PCI has a positive effect at higher quantiles, reaching a maximum of around 0.6, indicating that the industrial sector benefits from physical risk under favorable conditions.

Fig. 4, Fig. 4 illustrate the TCI's impact on the industrial market, exhibiting a pronounced divergence across quantiles. At lower quantiles, the effects are significantly negative, which relates to an inability to capitalize on transitional risks at low-performance levels in TCI and Industrial. For medium quantiles, the effects shift from neutral to slightly positive, indicating some capacity for the sector to adapt to moderate transitional risk. The effect becomes significantly positive at high quantiles, indicating the potential for the industrial sector to capitalize on transitional risks, perhaps through investments in sustainable technologies and infrastructure.

The effect of TCI on the Telecom sector, as presented in Fig. 3.13, remains marginally negative at lower quantiles, suggesting that the sector is sensitive to transitional risks during weak performance. The effect stabilizes as quantiles increase, enters positive territory at a higher quantile level, and peaks at around 1.2, revealing the Telecom sector's capacity to leverage initiatives to combat transitional climate risks under favorable conditions. Specifically, for Telecom, in evaluating PCI's effects at lower quantiles, it reveals that the reaction is primarily negative, with a value of about −0.6, indicating that all physical risks are, to some extent, critical in weaker market situations. The effect reaches around three at the upper quantile and appears quite resilient, as indicated in Fig. 3.14. The Utilities sector also adapts under TCI comparison, as shown in Fig. 3.15. Effects are modestly negative at lower quantiles, implying difficulties amidst weak performance. The sector has shown resilience, with positive impacts more evident at higher quantiles (i.e., positively peaking at 0.7). In Fig. 3.16, the results highlight the sector's ability to capitalize on transitional risks through innovation and sustainable energy strategies. For the utility sector, pronounced negative effects are observed at lower quantiles across PCI (−0.3), consistent with its susceptibility to extreme physical risks. However, the sector's resilience is also notable, with perturbations being damped when they stabilize and favorable effects turning positive at the 75th quantile.

Fig. 4, Fig. 4 present the effect of PCI and TCI on the MSCI index. Regarding TCI, at lower quantiles for both TCI and MSCI, the effects remain negative but of slightly lower magnitude compared to the m-QQR results, suggesting that without OVX, the negative impact of transitional risks is mitigated for weak market conditions. The effect returns to neutral at medium quantiles, taking values close to zero, which aligns with the m-QQR results. The MSCI index retains the benefits of transitional risks even at upper quantiles, peaking near 0.4, so strong performance has a contractive effect on our model despite OVX not being a stabilizer. For PCI, the QQR results show fairly consistent negative effect patterns at lower quantiles, eventually approaching a value of −0.6. This shows the MSCI index's susceptibility to physical risks during a market downturn, although excluding the OVX does reduce this effect relative to the m-QQR results. The effect becomes positive at upper quantiles, reaching a maximum of approximately 0.6 and reaffirming the MSCI index's ability to exploit physical risks in favorable market years.

In this sub-section, we conducted additional tests to substitute the PCI and TCI with the surface temperature (TMP) in Qatar. Climate change is measured by the average weekly surface temperature (TMP) in Qatar, expressed in degrees Celsius, as reported by the Qatar Meteorology Department and verified through NOAA's Climate and Data Online platform. Most importantly, we perform this robustness check on the same sample span (2019–2024), which includes the COVID-19 pandemic, the Russia-Ukraine crisis, and current geopolitical tensions affecting maritime trade routes in the Red Sea.