Investment Model Research Articles

Spatio-temporal variations of habitat quality in the Greater Bay Area around Hangzhou Bay, based on LUCC and simulation

Context Habitat quality (HQ) is vital for achieving sustainable regional development. Understanding the spatial patterns and temporal evolution of HQ in the context of land use–cover change (LUCC) is crucial for promoting ecological civilisation and high-quality growth, especially for regions with rapid economic development. Aims In order to analyse the impact of LUCC on habitat quality in the coastal areas with frequent human activities. Methods This study focused on the Greater Bay Area around Hangzhou Bay and analysed the HQ on the basis of LUCC data during 2010–2020 by using the InVEST model and spatial autocorrelation analysis. Additionally, land-use data for multiple scenarios in 2030 were predicted using the PLUS model, and the variations in land use and HQ in the study region during 2020–2030 were assessed. Key results During 2010–2020, the construction land in the region expanded by 1932.79 km2, primarily at the expense of cropland and water areas. The mean HQ values were 0.6287, 0.6181 and 0.6037 for 2010, 2015 and 2020 respectively, indicating a continuous decline. Spatially, HQ exhibited strong clustering during this period. However, there was a clear trend of fragmentation and reduction in ‘high–high’ cluster areas along the coast, mostly owing to the transformation of water areas and wetlands into construction land and cropland. In the projected scenarios (natural development, ND; economic development, ED; crop protection, CP; and ecological protection, EP) for 2030, the mean HQ values are estimated to be 0.5881, 0.5837, 0.5915 and 0.5965 respectively. Compared with 2020, there will be a certain decrease in HQ, with the EP scenario showing the lowest decrease of 0.0052. Conclusions The HQ changes were closely linked to LUCC, the construction-land expansion was the main cause of HQ destruction in the Greater Bay Area around Hangzhou Bay. To alleviate the trend of declining HQ, it is essential to select appropriate development scenarios for each city in the region and coordinate the development of the cities. Implications These findings provide valuable insights for promoting sustainable economic growth in the Greater Bay Area around Hangzhou Bay.

Spatiotemporal variations of ecosystem services in the Aral Sea basin under different CMIP6 projections

The Aral Sea, located in Central Asia, has undergone significant reduction in surface area owing to the combined impacts of climate change and human activities. This reduction has led to a regional ecological crisis and profound repercussions on ecosystem services. Investigating the spatiotemporal variations and synergistic trade-offs of ESs in the Aral Sea basin is crucial for fostering the integrated development of the region’s socioeconomic ecology. This study utilizes the Future Land-Use Simulation and InVEST models to analyze future land-use scenarios, integrating CMIP6 projections to assess the quality of four key ecosystem services: water production, soil conservation, carbon storage, and habitat quality over two timeframes: the historical period (1995–2020) and the projected future (2021–2100). Employing Spearman correlation, the study explores the trade-offs and synergies among these ecosystem services. Findings reveal that the primary forms of land-use change in the Aral Sea basin are the reduction in water area (− 49.59%) and the rapid expansion of urban areas (+ 504.65%). Temporally, habitat quality exhibits a declining trend, while carbon storage shows an increasing trend, and water production and soil retention fluctuate initially decreasing and then increasing. Spatially, water production and carbon storage demonstrate an increasing trend from the northwest to the southeast. Habitat quality exhibits a higher spatial pattern in the southeast and south, contrasting with lower spatial patterns in the north and west. Low-level soil conservation is predominantly distributed in the northwest, while medium to low-level soil conservation is prevalent in the east of the basin. The trade-off and synergy analysis indicates that between 1995 and 2020, a trade-off relationship existed between carbon storage and habitat quality and water production, whereas synergies were observed between soil conservation and carbon storage, water production and habitat quality, and soil conservation. The correlation between water production and soil conservation emerges as the strongest, whereas the correlation between carbon storage and habitat quality appears to be the weakest. The dynamic spatiotemporal changes, trade-offs, and collaborative relationships of ESs constitute major aspects of ecosystem service research, holding substantial implications for the effective management of the regional ecological environment.

Ecosystem services dynamics and their influencing factors: Synergies/tradeoffs interactions and implications, the case of upper Blue Nile basin, Ethiopia

Understanding the relationships among ecosystem services (ESs) and their interactions with influencing factors is essential for spatially targeted ecosystem governance. However, classifying the spatial distribution of these diverse interactions still needs improvement. Furthermore, existing studies have insufficiently addressed the specific impacts of bidirectional land cover transitions on ESs. Taking the upper Blue Nile basin as a study area, we estimated the spatiotemporal distribution of annual water yield (AWY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, using InVEST models and associated formulas. Changes in ESs per inward-outward land cover transition were quantified based on the Cross-Tabulation Matrix. An improved pairwise method was employed to assess the spatially diverse interactions between ESs pairs and their relationship with influencing factors. The statistical significance of influencing factors was evaluated using partial least square regression. The findings indicated that high HQ values were prevalent in the west, while they were in the east for SR. The central and southern areas experienced higher CS and AWY values. During the study period, variations were observed in the mean values of SR (ranging from 22.89 to 23.88 × 102 t/ha/y), AWY (32.13–42.2 × 102 mm/ha/y), CS (90.5–102.9 × 103gC/ha/y) and HQ (0.62–0.64). Synergies were predominant in AWY-CS, AWY-SR, and CS-SR pairs. HQ revealed more of a no-effect and tradeoff relationship with other ESs. The interactions between ESs and influencing factors were dominated by synergies, followed by tradeoffs and no-effect. The influence of landscape structure (gyrate and landscape shape index) and land surface temperature on all ESs and precipitation on AWY and SR was significant (1.049 ≤ Variable Importance in the Projection ≤ 1.371). Overall, the spatiotemporal dynamics of key ESs and the modeling of their drivers are essential policy information for taking spatially explicit conservation measures. This study will also serve as a valuable methodological reference for future research.

Evolution characteristics and multi-scenario prediction of habitat quality in Yulin City based on PLUS and InVEST models

As a major energy city in China, Yulin City has faced huge challenges to the ecological environment with its rapid economic development and rapid urbanization. Therefore, it is of great significance to study the impact of land use changes on habitat quality. Based on three periods of land use data in Yulin City in 1995, 2005 and 2015, the PLUS model was used to simulate the land use changes in 2015. The measured kappa coefficient was 0.8859, which met the simulation accuracy requirements. By setting development zone boundaries and adjusting parameters, three progressive scenarios are designed to predict the spatial distribution of land use in Yulin City in 2035. The InVEST model was used to analyze the spatiotemporal evolution of Yulin City’s habitat quality in the past 20 years and evaluate the distribution of Yulin City’s habitat quality under three scenarios after 20 years. The results are as follows: (1) During the study period, construction land in Yulin City expanded rapidly, with an area increase of 380.87 km2 in 20 years, and ecological land gradually shrank. (2) The land use simulation results of Yulin City under various scenarios in 2035 show that future land use changes in Yulin City will mainly be concentrated in the central and western regions. (3) During the study period, the habitat quality of Yulin City was at a medium level and the overall habitat quality showed a downward trend. Spatially, the degree of habitat quality degradation in Yulin City showed a characteristic of gradually decreasing from West to East. (4) By 2035, under the scenario of suitable urban economic development, Yulin City’s habitat quality has been improved to a certain extent, which not only protects ecological security but also meets the demand for construction land for urban development. The results of this study help the government better understand the evolution of land use and habitat quality in Yulin City in the past 20 years, and provide theoretical support and reference for the formulation of Yulin City’s ecological environment protection policies and the implementation of ecological protection work under the current land spatial planning.

The Bright Side of Political Uncertainty: The Case of R&D

Abstract We use close gubernatorial elections as a quasi-natural experiment to document a positive effect of political uncertainty on firm-level R&D. This finding is in contrast to the existing literature documenting a negative impact of political uncertainty on capital investment. We examine potential mechanisms and find that our results are consistent with the growth option view of R&D investment. The effect is stronger for politically sensitive and high-tech industries.The results are robust to different proxies for political uncertainty shocks. As predicted by models of investment under uncertainty, the real effects of political uncertainty critically depend on the type of the investment. JEL G18, G38, O31, O32

Spatiotemporal Evolution and Future of Carbon Storage in Resource-Based Chinese Province: A Case Study from Shanxi Using PLUS–InVEST Model Prediction

Resource exploitation markedly alters land use and ecological carbon storage, posing risks to carbon sinks and food security. This study analyzes land-use change from 1990 to 2020 in the resource-based province of Shanxi, China. By introducing a mineral resource driver, the PLUS model was used to predict four scenarios: natural development (ND), cropland protection (CP), ecological protection (EP), and dual protection of ecology and cropland (DP). The spatial and temporal evolutions of carbon storage were then analyzed using the InVEST model. Forests were predominantly distributed in mountainous areas, with croplands in southerly and central flat areas, construction lands in and around cities, and mining lands sporadically distributed across Shanxi. From 1990 to 2020, croplands and grasslands decreased, while forest, construction, and mining lands increased. Carbon storage decreased continuously, with a total loss of 15.1 × 106 t. High-value carbon storage areas were in the Lüliang, Taihang, and Taiyue Mountains, and low-value areas were in the more populous central and southern regions. Carbon storage was predicted to decline by 2035 under the ND and CP scenarios and to exceed that of 2020 under the EP and DP scenarios. The DP scenario projected an increase of 4.93 × 106 t in carbon storage by 2035. The DP scenario realizes the protection of carbon sinks in resource-based areas and maintains food security, providing a theoretical reference for achieving carbon neutrality and high-quality sustainable development in Shanxi Province.

Portfolio Management of Consumer Industry Based on Index Model

This paper explores the application of the index model in portfolio analysis, highlighting its significance in both macro and security analysis for the optimization of portfolios. Utilizing data spanning from October 2018 to November 2023, the study draws several key conclusions. Firstly, it identifies the optional consumption industry as particularly susceptible to market fluctuations, exhibiting higher sensitivity compared to the daily consumption industry. Secondly, an active portfolio comprising Costco, Walmart, Lululemon, and Under Armour, alongside short positions in risk-free securities, yields the optimal portfolio with a maximized Sharpe ratio. However, the study acknowledges limitations, notably the relatively small sample size and the impact of the COVID-19 pandemic, which introduces extreme risks and potential errors in the analysis. Future research directions include modifying the index model to incorporate volatility adjustments and exploring multi-factor investment models to mitigate errors and enhance portfolio efficiency. This study underscores the importance of continuous refinement and adaptation of analytical frameworks in portfolio management amidst dynamic market conditions.

Comparison of Markowitz Model and Index Model in Different Constraints

This paper reviews the application of Markowitz model and index model in portfolio optimization and focuses on comparing the performance under different constraints. The study covers the core concepts and theoretical framework of modern portfolio theory and the development of the two models. The Markowitz model employs quantitative analysis to consider the expected return and risk of assets and aims to maximize investor utility. The index model, on the other hand, achieves investment objectives by replicating the performance of a specific index. Comparative analysis shows that the Markowitz model is suitable for active management, while the index model is more suitable for passive investment. With the advancement of computing technology, the application of Markowitz model will be more accurate and extensive. Meanwhile, as the concept of passive investment becomes popular, the index model will continue to attract more investors. This study is of great significance to investors in choosing investment strategies, and will help academia and the industry to further optimize investment models.

A landscape index for indicating the spatio-temporal dynamics of carbon storage in an opencast coal mine

Coal is China’s main resource, with open-pit mining accounting for a significant portion of global production. However, this activity, including mining and ecological restoration, can have a definite impact on ecosystem carbon storage and its distribution; its associated factors are also unclear. In this paper, we quantify the carbon storage changes in Haerwusu coal mine, a typical large-scale coal mine in China, based on land use/land cover (LULC) characteristics, and analyze the impact factors of carbon density from 2007 to 2022 by integrating the InVEST model with the landscape ecological function contribution ratio and multiple regression model. The results are as follows. (1) Carbon storage decreased from 159.95 × 104 to 147.51 × 104 from 2007 to 2017 and then increased to 151.91 × 104 to 2022. (2) The degree of coordination between carbon storage forest and grassland area landscape pattern coupling ranged from 0.887 to 0.867 from 2007 to 2022, with the lowest point at 0.720 in 2012. (3) Carbon storage was significantly related to vegetation indices, temperature, and elevation, and these factors can explain 37.5% of the carbon storage spatial variability; stepwise regression analysis showed that the integration of landscape patterns, such as Shannon’s diversity index (SHEI) and the aggregation index (AI), could improve the explanation by 1.4%. (4) Based on the analysis of the landscape ecological function contribution ratio, the carbon storage-sensitive areas can be classified into three levels: extremely sensitive areas ranging 0 to 4 km from the mine, sensitive areas ranging 4 to 8 km, and insensitive areas ranging beyond 8 km. This study proposes a strategy for analyzing changes of carbon storage in coal mines, highlights the important role of landscape patterns in influencing carbon storage, and provides a reliable reference support for the ecological management of coal mines.

Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.

Uncertainty, precautionary saving, and investment: Evidence from prescheduled election cycles

AbstractWe show empirically that firms increase cash holdings starting as early as one year before prescheduled (i.e., predictable) elections. Then, for four quarters around elections when uncertainty and external financing costs are high, firms decrease investment and draw down saved cash balances to avoid tapping external financing. We use a dynamic model of firm investment and saving to demonstrate the importance of anticipation of future financing costs to firms' pre‐election precautionary saving behavior. Theoretically, if election uncertainty were only to affect potential investment, lower firm investment would result in higher contemporaneous cash balances, which is inconsistent with our empirical results.

How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China

Reducing soil erosion (SE) is crucial for achieving harmony between human society and the ecological environment. The cultivated land fragmentation (CLF), directly or indirectly, alters soil structure, diminishes its water-holding capacity, and escalates the risk of SE. Scientific assessment of the effect of CLF on SE can provide new insights into controlling of SE across watersheds in China. However, few studies have quantified the effect of CLF on SE. Therefore, we utilized land use change data in the Yangtze River basin from 2000 to 2020, measuring the levels of CLF and SE using Fragstats and InVEST models. The bivariate spatial autocorrelation model was employed to reveal the spatial relationship between CLF and SE. Additionally, we constructed a spatial Durbin model and introduced the geographically and temporally weighted regression model to analyze the role of CLF on SE. The south bank of the upper and middle reaches of the Yangtze River basin exhibited high CLF and SE. The bivariate spatial autocorrelation results showed a significant positive spatial correlation between CLF and SE. The spatial Durbin model results showed that CLF had a spatial spillover effect and time lag on SE, and the effect of CLF on SE had an inverted “N" curve. The study also confirmed that last SE and neighboring SE areas influenced local SE. Currently, CLF had a negative effect on SE in the Sichuan Basin, Yunnan-Guizhou Plateau, and the middle and lower Yangtze River Plain, and positively in Qinghai, Hunan, and Jiangxi provinces. These findings suggest that the government should enhance cross-regional and cross-sectoral cooperation and monitoring of cultivated land changes to prevent and control SE effectively.

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region’s habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively.

Returning Cropland to Grassland as a Potential Method for Increasing Carbon Storage in Dry-Hot Valley Areas

A key aspect of mitigating global climate warming is enhancing the carbon storage capacity of terrestrial ecosystems. China’s Grain for Green Program (GFGP) is the largest ecological restoration project in the world, which is closely associated with land use change. A systematic assessment of the GFGP’s impact on regional carbon storage is of great significance for promoting regional development and maintaining ecosystem stability. Therefore, this study selects a typical dry-hot valley area—Yanjin County—as the study area, which serves as an ecological protection barrier in Southwest China. We employed the InVEST model and Geo-detector model based on land use data from three periods (2000, 2014, and 2019), combined with static overlay analysis methods, in order to evaluate the impact of the implementation of GFGP on the spatial and temporal distribution of carbon storage. We also explored the driving factors of the spatial differentiation of carbon storage. The results indicate that, since the implementation of the GFGP, a total of 180.03 km2 of cropland has been converted to forestland, increasing the forest cover rate from 81.83% to 83.37%. The project has contributed 5.88 × 105 t to regional carbon storage, effectively offsetting carbon emissions caused by human activities such as urban expansion while also promoting the growth of regional carbon storage. The implementation of the GFGP has led to changes in three types of land use. Among them, converting cropland to forestland (3262 t/km2) is the most effective carbon sequestration method, and converting cropland to grassland (2530 t/km2) has shown great potential in carbon sequestration. Additionally, the study found that elevation (0.038–0.059) is the main factor affecting the spatial differentiation of carbon storage, and the interaction between elevation and other factors can effectively enhance the carbon sequestration capacity of regional ecosystems. Overall, the GFGP not only plays a significant role in combating climate warming but also makes an important contribution to improving the stability and sustainability of regional ecosystems.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

The impact of climate policy on oil and gas investment: Evidence from firm-level data

Using a text-based firm-level measure of climate policy exposure, we show that climate policies have led to a global decline of 6.5 percent in investment among publicly traded oil and gas companies between 2015 and 2019, with European companies experiencing the most significant impact. Similarly, climate policy uncertainty has also had a negative impact. Our empirical results support the Neoclassical investment model. According to this model, firms pre-emptively cut investment in response to downward shifts in future demand. These findings contrast with the Green Paradox theory, which predicts an increase in current investment by oil and gas firms aimed at shifting extraction toward the present.

Evaluation of urban flood adaptability based on the InVEST model and GIS: A case study of New York City, USA

Evaluation of urban flood adaptability based on the InVEST model and GIS: A case study of New York City, USA

Investments, identities, and Chinese learning experience of an Irish adult: the role of context, capital, and agency

Abstract This case study aims to explore the foreign language (FL) investments of a highly motivated young Irish adult in learning Chinese across different contexts, encompassing classroom settings and daily life, both in Ireland and abroad. By analysing the interview data through the lens of Darvin and Norton’s model of investment, this study shows that the participant’s investments at different stages of her learning journey appear to be intricately intertwined with her identities and are mediated by the perceived likelihood of achieving her imagined identity. This study highlights the importance of present identity and linguistic capital in shaping and consolidating native English speakers’ FL-related identities.It provides insights into how FL learning investment and FL-related identitiesare influenced by learners’ first language (L1) in the era of globalisation. As powerrelations among individuals with different L1s are often unequal in the globallinguistic marketplace, learners’ L1 can be valuable capital that influences the return of foreign language learners’ investment.

A system dynamics approach for cost-benefit simulation in designing policies to enhance the cybersecurity resilience of small and medium-sized enterprises

The small and medium-sized enterprises (SMEs) with limited investment capacity are likely to be lax in enhancing their cybersecurity. Therefore, to strengthen cybersecurity at a national level, governments must intervene in the market by using support or regulatory policies to overcome market failures and address weaknesses. This study reviewed the efficiency of policy options to improve corporate cybersecurity resilience for SMEs that require government support, unlike large companies that can invest in security on their own. To achieve this, a causal loop diagram was created and analyzed from the perspective of system dynamics. The model incorporated government support variables and the decline in capabilities over time into the existing corporate security investment model reflecting the standard framework for cybersecurity from NIST. The simulation scenarios were constructed based on policy options considered by the Korean government. These include 1) pre-incident or post incident support services, and 2) management through tax credits and regulation. The results indicated that incentives, specifically tax credits, rather than regulation, were more effective in strengthening cyber resilience. This study describes the investment and internal capability development of a company affected by government policy, which is an external factor, and changes in profits can be observed by adding the company's profits and costs as variables. This profit variable allows for the comparison of a company's cyber resilience across scenarios. Additionally, if the government provides direct support immediately after a hacking incident, the company can recover more quickly. If these benefits are known and if the reporting of hacking damage is activated, cyber threat visibility will be secured by revealing hacking attacks that have been secretly conducted. Governments can use cyber threat visibility to strengthen national cybersecurity.