Economic Metrics Research Articles

Multidisciplinary Wind Farm Electrical Infrastructure Optimization

During the construction stage of a wind farm, it is paramount to consider which layouts are the most efficient. Recent literature has found that optimization of the annual energy production is not necessarily driven by the same factors as other economic metrics, such as the internal rate of return. One parameter that makes this particularly true is the electrical cable length and layout, which affect both the cash flow of the project and the investments. While placing turbines far from each other is beneficial to mitigate the wake effect, the capital expenditures associated with the longer cables affects economic metrics. On the other hand, even though placing turbines close to each other reduces cable expenses, wind turbine mechanical loads are challenging to determine from wake effects. This work investigates trends between annual energy production, internal rate of return, electrical cable layout, and turbine components degradation due to mechanical loads. Results point towards corroborating recent literature, finding financial opportunities to explore tighter layouts with shorter electrical cables.

Broad-Spectrum Technical and Economic Assessment of a Solar PV Park: A Case Study in Portugal

While technical optimization focuses on maximizing the annual energy yield of utility-scale PV parks, the ultimate goal for power plant owners is to maximize investment profit. This paper aims to bridge the gap between technical and economic approaches by using simulation data from a real-case utility-scale PV park. It analyzes how changes in configuration parameters such as the DC–AC ratio and string length and PV technologies like solar tracking systems and bifacial modules impact the economic metrics of the project, i.e., net present value (NPV) and internal rate of return (IRR). PVSyst software was utilized as a simulation tool, while in-house developed software implementing appropriate technical and economic models served as a comparison platform and was used to validate the outputs generated through PVSyst. Results indicate that the commonly used horizontal single-axis tracking configuration may economically underperform compared with fixed-tilt setups. The optimal DC–AC ratio fell within the range of 1.30 to 1.35. Extending the string length from 25 to 28 modules improved economic indexes. Additionally, fixed-tilt bifacial modules can enhance project economics if a 10% cost premium compared with standard monofacial PV modules is considered.

Techno-economic assessment of green hydrogen production in unexploited landlocked northern-western region of Oman to realize national sustainable strategy

The sustainable and economical production of hydrogen is pivotal to the energy transition. This study assesses the techno-economic feasibility of green hydrogen in the unexploited northwest landlocked regions of Oman, capitalizing on unused renewable resources to facilitate a sustainable energy transition. In this study, a renewable energy system and green hydrogen production facility are sized to meet a fixed demand of hydrogen (100 metric tons per day) across five potential areas. Technical and economic metrics are then evaluated for various renewable energy combinations. The results reveal the lowest levelized cost of hydrogen in a standalone solar energy scenario, ranging from $8.34 to $16.73 per kilogram of hydrogen. Additionally, the levelized cost is projected and benchmarked against internationally reported values by IEA and IRENA, offering insights into the system's economics and guiding advancements in technology and policy. Further, the produced green hydrogen is anticipated to play a pivotal role in decarbonizing the nearby petroleum fields, small and medium enterprises, and domestic infrastructure. Consequently, this research aligns with Oman Vision 2040 and contributes to Oman's National Net-Zero Strategy 2050, as well as United Nations Sustainable Development Goal 7 (affordable and clean energy) and 12 (responsible consumption and production).

Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

Soil carbon tonne-year accounting: Crediting the additional time-integrated amount of carbon captured in soil

Soil organic carbon (SOC) sequestration has become a critical component of climate change mitigation strategies, offering a natural and economically viable means to mitigate atmospheric CO2 levels. Current practices in SOC sequestration auditing face limitations due to the requirement for carbon permanence, which can discourage landholders from participating due to long-term commitments and uncertainties. We propose the concept of the Soil Carbon Tonne-Year as a new unit of measurement for assessing SOC sequestration, focusing on the time-integrated amount of carbon stored in the soil. Soil carbon tonne-year measures SOC stock across different operational soil carbon pools (such as Mineral Associated Organic Carbon and Particulate Organic Carbon), each with its own mean residence time. This approach, based on physical rather than economic or climatic metrics, aims to offer a more accurate, flexible, and realistic method of accounting for SOC. Our examples suggest that the Soil Carbon Tonne-Year approach could significantly enhance management flexibility, potentially increasing land value and leading to sustainable gains over the long term.

Cyber-Physical Systems as a New Frontier for Economic Security: The Impact of Advanced Data Capturing and Networking Technology on National Economies

This study investigates the burgeoning interface between cyber-physical systems (CPS) and economic security, a vital yet underexplored domain. With the advent of sophisticated data capturing and networking technology, the paradigm within which national economies operate is shifting. This research delineates the ramifications of such technologies on economic stability, employment trends, and national gross domestic product (GDP), alongside the emergent cybersecurity implications. The methodology encompasses a cross-sectional analysis of various economies that have adopted CPS at differing scales and capacities. Through quantitative data, the study reveals trends and patterns in economic performance metrics post-adoption of advanced data capturing and networking infrastructures. Complementarily, qualitative assessments from expert interviews offer insights into the broader economic and security implications. Preliminary findings suggest that the integration of CPS correlates with a multifaceted impact on economic security, including the creation of new industries and the obsolescence of traditional sectors. A notable rise in efficiency and productivity across industries utilizing CPS is counterbalanced by the challenges of increased cybersecurity threats and job market disruptions. The study contributes to the discourse on economic security by framing CPS adoption not just as a technological upgrade but as a critical economic event with widespread and significant consequences.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy

This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining the role of artificial intelligence-enhanced multi-energy optimization in rural energy planning within the broader context of a sustainable energy economy. By proposing an innovative planning framework that accounts for geographical and economic disparities across rural regions, this study specifically targets the optimization of energy systems in X County of Yantai City, Y County of Luoyang City, and Z County of Lanzhou City. Furthermore, it establishes a foundation for integrating these localized approaches into broader national carbon-neutral efforts and assessments of green total factor productivity. The comparative analysis of energy demand, conservation, efficiency, and economic metrics among these counties underscores the potential of tailored solutions to significantly advance low-carbon practices in agriculture, urban development, and industry. Additionally, the insights derived from this study offer a deeper understanding of the dynamics between government and enterprise in environmental governance, empirically supporting the Porter hypothesis, which postulates that stringent environmental policies can foster innovation and competitiveness. The rural coal-coupled biomass power generation model introduced in this work represents the convergence of green economy principles and financial systems, serving as a valuable guide for decision-making in decisions aimed at sustainable consumption and production. Moreover, this research underscores the importance of resilient and adaptable energy systems, proposing a pathway for evaluating emission trading markets and promoting sustainable economic recovery strategies that align with environmental sustainability goals.

Small modular nuclear reactors: A pathway to cost savings and environmental progress in SAGD operations

Small Modular Nuclear Reactors (SMRs) offer a promising option for environmentally friendly bitumen recovery operations. The extraction of oil sands in Western Canada is vital for the economy, but traditional methods like Steam-Assisted Gravity Drainage (SAGD) contribute significantly to greenhouse gas (GHG) emissions. In SAGD, steam generation, primarily fueled by natural gas combustion, is the main source of emissions. Given the imperative to reduce carbon intensity, less emissive recovery methods are needed to sustain production and economic viability in Canadian oil sands. Currently, there are limited non-carbon alternatives for steam generation in oil sands applications. The utilization of SMRs for steam generation presents a clean alternative. In this study, we examine the feasibility of employing SMRs in in-situ oil sands recovery operations. Through standardized economic metrics and sensitivity analysis, we demonstrate that integrating SMRs into SAGD operations eliminates GHG emissions significantly and can potentially outperform conventional natural gas-based steam generation in terms of net present value, under certain operational scenarios. Hence, our findings indicate that SMRs hold promise for decarbonizing oil sands recovery processes.

Context-aware multi-modal route selection service for urban computing scenarios

The rapid urban population growth introduces novel challenges to urban mobility scenarios, requiring innovative and connected solutions to relieve traffic congestion and enhance transportation efficiency, using unusual roads and public transportation modals. For instance, multi-modal routes allow more flexible modal combinations to offer smart multi-modal mobility. In this context, the route selection service must rely on different context information, such as criminality, accidents, air quality, and others, where Internet of Things technologies introduce active and ubiquitous sensing of many contexts along the urban scenario, providing data for statistical analysis to offer a safer and healthier urban reality. However, designing an efficient multi-modal route selection service that considers different context information to offer personalized routes is important. In this article, we describe a context-aware route selection service that considers adequate contextual information to provide routes according to the user’s preference. The multi-modal route selection service applies a multi-criteria method to give different degrees of importance to each criterion based on the user profile (i.e., Worker, Green, Safe, and Tourist). We present extensive evaluation results from applying our multi-modal route selection approach to a London dataset. The approach successfully enables user-preferred transportation choices using four balanced selection profiles and ten route features. The proposed multi-modal route selection service demonstrates better performance in terms of economic, ecological, and time-saving metrics for each user profile compared to a greedy selection manner.

Impact of natural resource dependence on green technology development: Role of digital governance in mitigating resource-curse using big data

The pursuit of a green economy as a strategy to counteract the “resource curse' phenomenon represents a burgeoning paradigm shift. A sophisticated digital governance infrastructure is vital, serving as an integral mechanism for ensuring transparency, enhancing data efficacy, and fostering public involvement—all crucial components of sustainable environmental stewardship. In light of this context, our analysis examines the interplay between natural resource dependence and green technological innovation, as well as delineating the potential pathways interlinking the two, through an empirical assessment using provincial data from China spanning the years 2011–2021. Employing advanced techniques in big data text mining, we have devised a government digital transformation index calibrated to elucidate the impacts of digitalization on the twin variables of natural resource reliance and the propagation of green technologies. Our findings indicate a marked deleterious influence of natural resource dependence on the evolution of green technological initiatives at the regional level, a trend most pronounced in China's central and western provinces as well as in locales with comparatively low economic development metrics. Furthermore, our research ascertains that the impeding force of natural resource dependence on green technology ascension is primarily exercised through diminished regional research and development capabilities, stagnation of industrial structure progression, and limited openness to external economic integration. Finally, the study posits that the implementation of digital governance measures holds the capability to substantially mitigate the restrictive impact of natural resource dependence on green technology advancement. This underscores the imperative for a digital governance architecture that actively neutralizes and meticulously monitors the adverse outcomes associated with resource consumption.

Critical Indicators on Macro Economics adoption in GDP: A systematic review

The study's goal is to investigate how important macroeconomic factors affect certain African nations. These metrics are crucial for the economies' effective development and can be utilized to address the many economic issues that the chosen countries are currently dealing with. 91 publications published between 2004 and 2022 were examined using the Scopus database. With an emphasis on five major factors: Growth Domestic Product (GDP), Interest Rate, Exchange Rate, Inflation, and Foreign Direct Investment this study evaluated and analyzed the literary elements and themes explored in order to give guidance for future research. The findings revealed that Interest rates, inflation, exchange rates, and foreign direct investment (FDI) are some of the macroeconomic variables that can affect the selected African economies. Foreign direct investment has been identified as the most important factor in improving industrial prosperity and living standards in developing economies such as Nigeria, South Africa, Egypt, Algeria, and Morocco after stabilizing interest rates, inflation and currency exchange rates. The study's findings are supported by material that has been published in previous years. Given that a number of recent economic issues have had an impact on the expansion of the economy, it is necessary to evaluate the ideas using panel data from the past 20 years in order to ascertain whether they are still valid. The report highlights the key macroeconomic factors that may have an impact on five different African economies. The study combines a number of economic metrics that have previously been studied separately to assess the trend in the economies it has chosen. To the best of my knowledge, this study is one of the few that evaluates both the individual countries used and the indicators in general.

Design and Economic Analysis of a Solar Thermal Pre-Cooling System for Agro-Cold Chain in Lesotho

The agricultural sector in Lesotho grapples with significant challenges regarding post-harvest losses. Given that 40% of all agricultural products require cold storage, food quality is compromised due to lack of cold storage to extract the heat from exposure to high field temperatures after harvest. This research proposes a solar thermal cooling system tailored to the specific needs of preserving fresh agricultural produce, leveraging Lesotho’s abundant solar energy resources. Through TRNSYS simulation and MATLAB economic analysis, optimal system parameters are determined, ensuring both technical efficiency and financial viability. The outcomes indicate that the proposed absorption solar thermal cooling system, incorporating evacuated tube collectors and an auxiliary boiler, effectively manages a cooling load of 7.318 kW, preserving fresh vegetables at 6.1°C. The optimized design features a chiller with a Coefficient of Performance of 0.8, a collector area of 12 m², and a hot storage volume of 0.2 m³, maximizing solar energy utilization. Importantly, economic metrics such as Levelized Cost of Energy ($0.085/kWh), Net Present Value ($9,200), Discounted Payback Period (12 years), and Savings to Investment Ratio (achieving 1 in year 13) demonstrate the financial feasibility and profitability of the system. These findings underscore the potential of solar thermal cooling as a promising investment option for addressing refrigeration needs in Lesotho, offering a sustainable solution to mitigate post-harvest losses and enhance economic performance in the agricultural sector.

Academic Productivity in Ophthalmology and Its Correlation to National Economic Indicators Among the OECD Countries: A Bibliometric Analysis

ABSTRACT Purpose Prompted by the clinical concern that limited healthcare resources allocation affects physicians’ research productivity, this study examines the association between bibliometric indices of ophthalmologic research and national economic indicators in Organisation for Economic Co-operation and Development (OECD) countries. Methods The Scimago Journal and Country rank source was searched for research productivity data in ophthalmology among OECD countries between 1996 and 2019. Bibliometric indices included: documents number, number and percent of citable documents, citations number, citations per document, and H-index. The updated economic indicators of each country (gross domestic product [GDP] per capita, health spending as percent of GDP (health expenditure), gross domestic expenditure on research, and development as percent of GDP [GERD]) were collected from the World Bank and the OECD websites. Correlation between economic and bibliometric metrics and multivariate linear regression analyses were performed. Results Among 267,444 documents analyzed, correlation analysis found a strong correlation between health expenditure and H index (r = 0.711, p < 0.001); a moderate correlation between health expenditure and documents number (r = 0.589, p < 0.001), number of citable document (r = 0.593, p < 0.001) and citations number (r = 0.673, p < 0.001); and a moderate correlation between GERD and H index (r = 0.564, p < 0.001). Multivariate regression analysis controlling for economic factors, population and language showed the independent association of these parameters with bibliometric indices. Conclusions This study demonstrates a positive correlation between bibliometric indicators of ophthalmology research and economic factors, particularly health expenditure, among the OECD countries. Our results suggest an advantage of domestic investment in health to expand academic productivity in the field of ophthalmology.

Beyond Data: Strategically Leveraging Consumer Metrics and Market Signals of Business Prosperity in the Context of Marketing Communications

In the dynamic environment of global markets, understanding the complexity of the relationships between economic indicators and consumer behaviour, which can be deliberately influenced by targeted communication activities, is becoming a decisive competitive advantage in achieving economic prosperity for businesses. The aim of the study is, based on market data correlation, to explain and approach the interrelations of selected economic attributes and metrics of consumer behaviour. The inquiry is conducted in the scope of optimisation determination of the allocation of the purchase gradient of a business unit in the context of maximising the effect of communication and service activities oriented towards achieving economic prosperity of the business entity. The focus of the study reflects the application of the interconnectivity of market predisposition, characteristics of the target group and geographic servicing in the form of localisation of the business unit in the paradoxical situational model of Hotelling’s Law. The result of the study confirms the leveraging effect of transactional increase and the rationale of localised communication effect for influencing consumers’ purchase decision-making processes in the purchase pairing of a business unit.

Household Activities and Greenhouse Gas Emissions Value: Lessons from a Neglasari Household Survey

The escalating threat of global warming stems from greenhouse gas emissions resulting from various household activities, profoundly impacting climate change. This study delves into Neglasari Village, Bogor Regency, scrutinizing emissions' correlation with household consumption. Predominantly focusing on carbon dioxide, methane, and nitrous oxide, emissions arise from electricity, cooking and oil fuel usage, waste, agriculture, and livestock farming. Objectives encompass elucidating and computing greenhouse gas emissions, discerning influential factors, and gauging their economic ramifications. Employing IPCC's 2006 emission valuation, multiple linear regression, descriptive analysis, and carbon economic metrics, findings reveal Neglasari Village's emissions total 1,247,364.80 kg CO₂-e/month, averaging 411.40 kg CO₂-e/month per household. Key influencers identified are household income and house ownership status. The calculated carbon economic value stands at IDR138,646/month per household, aggregating to IDR421,809,752/month across Neglasari Village, fortifying the imperative for sustainable practices.

Advancing the Adoption of Net Metering: An Economic Assessment of Grid-Tied Solar Photovoltaic Systems in Urban Homes in Ghana

Net metering schemes are pivotal in advancing grid-tied solar PV systems and promoting renewable energy adoption in developing nations. This study investigates the economic viability of implementing net metering within urban households in Ghana, considering the existing block tariff structure and a proposed time-of-use (ToU) structure for grid-tied PV systems. The study employed PVSOL premium (2023 R3) software for the economic performance assessment of the net metering schemes for simulated grid-tied residential PV systems in selected cities of Ghana’s Coastal, Forest, and Savannah climatic zones. The evaluation encompasses key economic metrics, including the levelized cost of electricity, accrued cash flow, annual cash savings, internal rate of return, and payback period. The study findings showed that, in most scenarios, the existing block tariff structure initially proved more economically favourable than the proposed two-tier Time-of-Use (ToU) structure. Consequently, financial investments were recouped within the project’s lifespan under net metering schemes based on the existing block tariff structure, whereas investments were not recouped under the ToU-based schemes. Nevertheless, factors such as improved daytime consumption, rising electricity tariffs, and aligning peak demand pricing hours with solar power generation hours emerged as potential drivers that could make ToU-based net metering schemes more attractive than block tariff structures. The study findings have significant implications for policy development related to tariff structures, particularly concerning the implementation of net metering for grid-tied solar PV systems within the residential sector of Ghana and other developing countries in sub-Saharan Africa.

Geospatially explicit technoeconomic assessment of sustainable aviation fuel production: A regional case study in Virginia

There is strong interest in sustainable aviation fuels (SAF) to decarbonize aviation; however, local decision-makers will need to consider what additional incentives could stimulate SAF commercialization within their own jurisdictions. This study analyzed SAF production in Virginia, evaluating two biomass-to-energy platforms (gasification Fischer Tropsch [GFT] and pyrolysis) and two regionally abundant feedstocks (woody wastes and municipal solid wastes). A suite of open-access modeling tools were applied to possible SAF supply chains encompassing feedstock collection and transportation, conversion, and fuel upgrading and transport. Key modeling outputs were minimum product selling price (MPSP) ($/gallon) and life-cycle global warming potential (GWP) (g CO2eq/MJ). Results suggest that early SAF production via GFT will require local incentives of approximately $3.61 per gallon compared to $0.75 per gallon for pilot-scale pyrolysis. Location of production facility (by county) influences economic and environmental metrics but is not nearly as important as facility size (tonnes/year). Different formats of financial incentives (i.e., tax credits, loan forgiveness, etc.) offer markedly different reductions in SAF MPSP. Finally, under current federal incentives in the US, it is still more economically efficient to use pyrolysis (with higher GWP) than GFT (with lower GWP). Therefore, regional stakeholders will need to navigate the tradeoff between economic and environmental performances of these platforms. Though Virginia was used as a case study, the methodology is replicable for other jurisdictions, insofar it can be adapted for use in other locations without decision-makers having to completely build their own TEA models.

Decision trees in cost–benefit analysis for flood risk management plans

ABSTRACT According to the European Directive 2007/60/CE, flood risk evaluation should include a cost–benefit analysis (CBA) on a long-term time horizon to evaluate the impact of mitigation measures. The standard CBA assumes to know in advance the events observed in the time horizon and a priori compares all mitigation measures by an economic metric. No change is supposed to be made to these measures throughout the time horizon. This modus operandi is not appropriate in the domain of flood risk management because several conditions are uncertain when the CBA is made (e.g., urban policies). This article faces these challenges by the integration of cost–benefit analysis and decision trees, to prescribe mitigation measures under uncertainty on the budget for mitigation actions because their funding can be modified after the conclusion of the CBA. The former integration is discussed in the real case of the lowland valley of the Coghinas River (Sardinia, Italy), for which the classical CBA compared five mitigation measures of infrastructural works. The integration into the decision tree also allows evaluation of mitigation measures with changes in infrastructural works and a lamination action. The outcomes advise decreasing the maximum storage level and increase the peak lamination.

Planning for a network system with renewable resources and battery energy storage, focused on enhancing resilience

The growing significance of network resilience underscores the importance of research in integrating Renewable Energy Resources (RESs) and battery energy storage Systems (BESS) with electrical networks. This paper presents a real-time simulation for systematically integrating renewable energy sources (RESs) and battery energy storage systems (BESS) in electrical networks, focusing on resilience metrics that involve a multi-objective optimization approach that considers the relative battery capacity to the total system cost. In addition, the Demand Response Program (DRP) is considered an additional layer to manage seven different loads that provide a comprehensive and accurate representation of energy consumption during 24 h. The scheduling model, which incorporates the IDR strategy, reduces the total load of data center performance by 40 %. Additionally, optimizing battery capacity can lead to an 89 % reduction in total costs, representing a substantial success in enhancing the economic resilience metric. This study has been conducted on the IEEE 24-bus RTN to assess the required investment for deploying BESS and to gauge its effectiveness in enhancing the resilience of the electrical network. The results confirm the effectiveness of the proposed model for enhancing the economic and performance metrics of resilience.