Cost-benefit Analysis Model Research Articles

Comprehensive Sensitivity Analysis of Cost-Benefit Analysis Variables for Transport Interventions

Cost-benefit analysis (CBA) is the universally applied tool to assess economic viability in assisting decisions on transport investments. Its framework is heavily influenced by the numerous variables it considers through estimating and valuing the intervention’s effects. This paper – utilising the authors’ previously implemented CBA test environment – comprehensively analyses the sensitivity of significant variables of three typical CBA models for transport interventions (road, rail and urban) to understand the prevailing appraisal approach better and to help focus on further methodological improvements. Morris and Sobol methods were selected to study the global sensitivity of and the relations between the input parameters of the models. The sensitivity test of the three analysed models provided similar results regarding which variables are most influential in CBAs. Input variables such as the investment cost, the economic discount rate, forecasted GDP changes and specific elasticities to these GDP changes often have a firm but mostly linear effect. Value of time, vehicle operating cost and mode choice-related parameters such as car availability, car occupancy rate, level of service indicators (e.g. frequency of service) and potential to induce travel demand (proxied by a ‘no travel’ parameter) are inputs with considerable linear effects and greater interactive effects.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice.

Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We tested the hypothesis that strategy transition in task learning results from cost-benefit analysis. Participants learned two task sequences and were then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-predicted strategy transition points are related to behavioral slowing and changes in fMRI activation patterns in the dorsolateral prefrontal cortex. Strategy transition is also related to increased pattern separation in the ventromedial prefrontal cortex. The cost-benefit analysis model outperforms alternative models (e.g., both strategies racing for being expressed in behavior) in accounting for empirical data. These findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

#1377 Evaluation of state-wide health system implementation of renal supportive care shows substantial net benefits

Abstract Background and Aims In 2016, the New South Wales (NSW) Health system implemented a service to make the principles and practice of palliative medicine comprehensively available to patients receiving chronic kidney disease (CKD) care across NSW. Summative evaluation of the economic value that this multi-disciplinary State-wide RSC service provided following implementation was prospectively planned in the domains of patient experience and economic value. This abstract reports the first cost-benefit analysis of a RSC service. Method De-identified patient-level data was extracted from the NSW Renal Supportive Care Register (a linked dataset comprising admitted patient, non-admitted patient, emergency department episodes and death registrations). Data was compared between 2015/16, prior to implementation of the State-wide RSC service; and 2018/19, following settled operation of the service. A cost-benefit analysis model was constructed to estimate the economic value of the RSC service between 2015/16 and 2018/19; and the projected value to 2029/30 under different scenarios. Multivariate Cox regressions were estimated to measure survival benefits, and a polynomial regression model was employed to estimate the impact of RSC on the share of patients receiving dialysis. Results Considering the actual impact of the NSW RSC service; in 2015/16, 5% (n = 443) of all ESKD patients, (defined as receiving dialysis >3 months, non-dialysis conservative care or in receipt of a functioning transplant) received any RSC service. By 2018/19, 16% (n = 1460) of all ESKD patients had received RSC within NSW. The proportion of patients receiving RSC from a non-dialysis conservative care or dialysis context remained stable between 2015/16 and 2018/19 at 41% and 29% respectively. Patients receiving RSC were estimated to have increased survival by 3-21 weeks, depending on patient characteristics, compared to the scenario of no RSC service. Considering the projected value of ongoing RSC services; scenarios with different assumptions were modelled. Assuming that the proportion of patients receiving different ESKD modalities and the costs of care remained stable, the RSC service would deliver a net financial benefit to the commissioning health system (avoided costs of service minus RSC costs) of ${\$}$109 million Australian Dollars (AUD), with a return on investment of 212%, compared to the scenario of no RSC service. When the economic value for the whole NSW community from increased survival benefit was included, the projected value was ${\$}$275 million AUD, with return on investment of 535%. The projected economic value of RSC was modelled under scenarios of increasing access to the service by ESKD patients (Figure). Increasing RSC access from the achieved 16% to 20% of ESKD patients was projected to deliver maximal economic value, with a 3% fall in ESKD patients receiving dialysis, increasing survival benefit (${\$}$221 million AUD) and economic value ${\$}$94 million AUD. As access to RSC expands further, modelling indicates that additional dialysis substitution becomes less likely but benefits still accrue from survival up to a maximum viable rollout of 50% of the ESKD population receiving RSC, when survival benefits plateau. Conclusion Economic evaluation of this implementation of the Statewide RSC service across NSW Health shows it has been associated with both a net economic benefit and survival benefits four years after implementation. It is projected to provide an economic value of ${\$}$275 million AUD over ten years, which would increase with greater participation of ESKD patients suitable for RSC.

Profit analysis of beef cattle business as agrotechnopark development effort in North Bolaang Mongondow Regency, North Sulawesi

This study aims to know the profit gain of the beef cattle business and the influencing factors. It used primary data and secondary data. The former was collected through questionnaires and the latter was obtained from the statistic office of North Bolaang Mongondow Regency. The sampling village was first selected using a purposive sampling method, then 30 respondents were randomly selected. Data analysis applied a cost-benefit analysis and regression model to detect the profit-influencing factors. Results showed that the mean profit of the beef cattle farmer per year was IDR. 2,241,893. Feed, calve, and labor simultaneously significantly affected the profit of the calve farmers in North Bolaang Mongondow Regency (α = 0.01).

Finding optimum reactive power compensation in a wind farm

Presently, renewable energies and especially wind energy are gaining a special relevance in the electrical market worldwide. This is due to the more and more urgent need to find alternative energies that supplant those derived from fossil fuels. Within this present framework of renewable energy development, it is worthy of mention the rapid advancement of wind energy and its notable penetration into the electrical systems of different countries, both in Europe and worldwide. This current rate of growth motivates the wind farms to not limit themselves to producing energy, but also to provide stability to the network within its capabilities, making then possible to increase the power percentage relative to the total power of the system. So, actual objective is to adapt the installations that produce wind energy in such a way that they give a maximum amount of support in any given moment to the electrical network. Throughout this study a complete cost-benefit analysis model is developed, focused on incorporating automatic capacitor banks into wind farms for the compensation of reactive power. The used methodology can be applied to the other technologies as well by simply amplifying the algorithms according to the specific characteristics of the option elected.

Integracija razvrstavanja sivih relacija te analize troškova i koristi za optimalno određivanje prioriteta održavanja asfaltnih kolnika

making problem. The common approach involves the prioritisation of road repairs with more extensive surface damages but this does not always align with the actual repair needs. To address this, a method based on the grey correlation theory was introduced to evaluate pavement performance. This method was employed to determine the optimal maintenance time for asphalt pavements. Once the sequence was established, the ideal maintenance schedule for the pre-maintenance sections was determined using a cost–benefit model. This evaluation method is particularly useful for quantitative analyses of the influences of pavement age and traffic volume on pavement performance parameters. The results demonstrate that grey relation sorting effectively weighs the factors and provides rankings for road segments requiring maintenance. A cost–benefit analysis model was used to assess the maintenance sections. This model can guide decisions regarding pre-maintenance sections and assess pavement conditions, thus resulting in outcomes that align better with real-world situations and human judgment. These findings are expected to be valuable for the long-term maintenance of asphalt pavements.

A Model Proposal of Cybersecurity for the IIoT: Enhancing IIoT Cybersecurity through Machine Learning and Deep Learning Techniques

The Industrial Internet of Things (IIoT) acceleration has caused automation and data exchange enhancements within industrial surroundings. Yet, this evolution has presented security issues due to the raised exposure of critical infrastructure to cyber threats. This study focuses on designing a thorough model for identifying and mitigating vulnerabilities within IIoT networks utilizing Machine Learning (ML) and Deep Learning (DL) techniques. A replicated IIoT network infrastructure was set to communicate and exchange data for simulation. Use of Python script to execute network scanning and data collection, distinct possible vulnerabilities. Then, ML-DL analysis is handled by employing techniques of gradient boosting, logistic regression, decision trees, random forest, multilayer perceptron, and convolutional neural network. Throughout, gradient boosting has proven to higher performance accuracy rate in recognizing the most impactful vulnerabilities. As well as a model integral part of a Cost-Benefit Analysis (CBA) provides security recommendations to mitigate identified vulnerabilities. According to the CBA model vulnerabilities are prioritized based on the severity, related costs, and potential benefits of mitigation. The proposed Cybersecurity model in addition to high accuracy in vulnerability detection also provides a standardized approach for categorizing Cybersecurity countermeasures according to costeffectiveness. This study emphasizes the need for a consolidated Cybersecurity model for the IIoT and shows the capability of ML techniques to advance Cybersecurity posture. Future work considered testing the model in a real operation environment of IIoT, refining the model, and enrichment with more knowledge base actionable mitigations.

Techno-economic feasibility analysis of a commercial grid-connected photovoltaic plant with battery energy storage-achieving a net zero energy system

Grid connected Photovoltaic (PV) plants with battery energy storage system, are being increasingly utilised worldwide for grid stability and sustainable electricity supplies. In this context, a comprehensive feasibility analysis of a grid connected photovoltaic plant with energy storage, is presented as a case study in India. A novel smart net-zero energy management system is developed to reduce grid and fossil fuel-based backup electricity consumption during power outages and peak load shaving by controlling peak load demand A life cycle cost-benefit and levelized cost of energy (LCoE) analysis, is presented for five optimised photovoltaic plants with battery energy storage systems, with and without net metering regimes. The Solar Labs and PVSyst softwares are used for system planning and energy generation estimation followed by HOMER grid software and Excel sheet-based financial models for system optimisation and cost-benefit analysis. The sensitivity analysis is carried out considering most sensitive parameters to identify the best option. The results found a 200 kWp photovoltaic plant with 250-kWh battery energy storage system with net metering, as the best-optimised option with energy generation cost of INR 4.21/kWh, with 6.15 years payback period. The study results can be followed for sustainable solar power generation for commercial grid connected PV power plants worldwide.

How WTO commitments tame uncertainty

This paper studies how WTO rules and flexibilities shape its members’ trade policy responses to import shocks. Guided by a cost–benefit analysis model and using a unique database of tariff bindings for all WTO countries over the 1996–2011 period, we show that WTO commitments do affect members’ trade policy. More stringent bindings reduce the likelihood of responding to import shocks by raising tariffs. In addition, consistent with cooperative behaviour, fear of retaliation also reduces the likelihood of tariff increases. In a counterfactual scenario where WTO members can arbitrarily increase tariffs, they are 4.3 times more likely to do so than under current bindings. The upshot is that WTO commitments contribute to a more stable trade policy environment.

An Empirical Study of Financial Efficiency and Stability of Shrimp–Mangrove Farming Model in Nam Dinh Province, Red River Delta, Vietnam

Shrimp–mangrove farming is a favorable production model suitable for the Red River Delta, Vietnam. This study was carried out in Nam Dinh province to evaluate the effectiveness and stability of the shrimp–mangrove farming model in the area. A participatory approach was carried out through surveys, focus group discussions, and interviews with 415 farming households in the Giao Thien and Giao An districts, the buffer zone of Xuan Thuy National Park, in 2022. We then used a cost–benefit analysis model to evaluate the financial performance and stability of the shrimp farming model. SWOT analysis was also used to identify opportunities and threats to this model. The research results showed that the shrimp–mangrove model has the advantages of low investment costs, diversified income sources, low risk, and environmental sustainability. However, the limitations of the model are low financial efficiency and not high stability. The main difficulties of the model are poor quality breeds, diseases, limited farming techniques of farmers, limited infrastructure system, the impacts of climate change, and low productivity. The study also proposes management implications to enhance the effectiveness and sustainability of shrimp farming in the Red River Delta, Vietnam.

Social Benefit-Cost Analysis for Electric BRTS in Ahmedabad

This research aims to develop a social benefit-cost analysis model for the feasibility assessment of the proposed electric bus rapid transit system project in Ahmedabad, India. An attempt has been made to indicate that the electric bus locomotion, which has a high potential for improving environmental impact can be considered sustainable for urban conglomeration. The realization of sustainable transportation projects is frequently hampered due to the high weightage associated with the financial dimension and somewhat negligence of environmental and social dimensions of the transportation system in the project appraisal process. Therefore, a quantitative analysis of the electric bus transportation system in an urban context was conducted, considering the three major dimensions of sustainability such as economic, environmental, and social. The evaluation was carried out using the present worth analysis of various benefits and costs associated with the implementation of the electric bus rapid transit system in the city. The outcome, as indicated by the value of social benefit-cost ratio, illustrates that such a project can be positively justified from the point of view of the benefits gained by the society as well as profitable returns and value addition of infrastructure investment in the long run. The research contributes by validating that social benefit-cost analysis can be used to evaluate sustainable transportation system appraisals in order to make their realization more favorable.

Whether it is economical to use combined heat and power (CHP) system for the efficient utilization of associated petroleum gas in oil extraction sites in China: A cost-benefit analysis considering environmental benefits

The efficient use of associated petroleum gas with combined heat and power (CHP) systems in oil extraction sites has proven to be technically feasible, but its economic inefficiency continues to deter oil companies from using CHP, given that simple fuel cost reductions do not yield significant positive returns in the face of large investments in purchasing and maintaining CHP. This research constructs a cost-benefit analysis model, which includes the monetized environmental benefits generated by CHP. A pilot experiment operated in a certain oilfield in Shandong province shows that the annual difference between the reduction in fuel costs and the investment in purchase and maintenance of CHP is only about ¥210,000 per device. However, when environmental benefits including health benefit and low-carbon benefit are included in the model, the annual environmental benefits of a single equipment update can be about ¥760,000, and the overall annual net benefits will reach about ¥970,000. It is concluded that the application of CHP in oil extraction sites is economically efficient, taking into account the environmental benefit it can produce. The research results will help oil companies use CHP to make more contributions to carbon and air pollutant emission reduction. However, considering that a large number of CHP systems can form a distributed energy structure, the proposed model still has limitations.

Managing Urban Eco-Spaces for Sustainable Social Value: A Case Study of Mangrove Ecosystem Services in Shenzhen, China

Against a background of high-speed development and the expansion of built land surface, there has been widespread encroachment of natural habitats (henceforth “eco-spaces”). It becomes correspondingly difficult to improve the value of urban ecosystem services, since current assessments of land still primarily focus on use value (e.g., when nature and its products are converted and consumed), while the regulation and non-material services provided by eco-spaces are often ignored. In order to assess the rate of return on land transfers of eco-space, theories such as strong sustainable development, ecological land rental, natural resource ownership and opportunity costs were synthesized to construct an eco-space transfer cost–benefit analysis model, which is based on the value of ecosystem services. This study used the Futian Mangrove Ecological Park in the megacity of Shenzhen as a case study. To estimate the social value, which more comprehensively captures ecosystem services and economic value from eco-space, we used a counterfactual scenario of residential land development (the most plausible alternative land use scenario). This allowed us to calculate the rate of return from eco-space use change. The result of the total and annual rates of return over a projected 70-year timeframe were −10.76% and −0.16%, respectively. Accordingly, we suggest that management of the mangrove park should change the pricing strategy to better align the social benefits that it generates with the payments sustaining it. This case study provides instructive lessons for the management of eco-space in large urban settings.

Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review

The question of whether and to what extent farmers can adapt to climate change has recently gained academic interest. This paper reviews contemporary econometric approaches that assess the impacts of climate change on agriculture and consider farmer adaptation, complementing previous methodological reviews with this distinctive adaptation perspective. The value of adaptation can be measured by comparing the differences between the long-term climate change effect and the short-term weather shock effect. However, this theoretical model has not yet been well supported by empirical evidence, as it is difficult to identify true adaptation, incorporating adaptation cost, and estimated adaptation rate. Quasi-natural experiments, cost-benefit analysis, and Bayesian models are effective tools to address these methodological drawbacks. Two methods dominate in the estimation of climate effects, but each has its own advantages. A good estimate provides a trade-off between the incorporation of farmers’ adaptive behavior and the reduction in omitted variables bias. Cross-sectional data models based on climate variability can capture farmers’ long-term adaptations but are prone to bias due to omitted variables. Panel data models are more effective at mitigating omitted variable bias by applying fixed effects, but do not consider farmers’ adaptative behavior to long-term climate change. To address this dilemma, several cutting-edge approaches have been developed, including integration with the weather and climate model, the long differences approach, and the long- and short-term hybrid approach. We found three key challenges, namely: (1) exploring adaptation mechanisms, (2) the CO2 fertilization effect, and (3) estimating the distributional effects of climate impacts. We also recommend future empirical studies to incorporate satellite remote sensing data, examine the relationship between different adaptation measures, model farmers’ future climate expectations, and include adaptation costs.

Road pavement energy harvesting: A technological, economical and cost-benefit analysis

ABSTRACT With the growing need for cleaner energy sources, research into the energy harvesting industry has increased over the last years. Roadways and transport infrastructure are promising contexts for energy harvesting due to their global coverage, being continuously exposed to everyday traffic-induced pressures. This study presents a model for Technical and Economic Analysis, as well as a Cost-Benefit Analysis model for Road Pavement Energy Harvesting systems. These models are then applied in a grid injection scenario, testing their applicability on a system developed by a startup company. Results show that the tested technology is economically viable for scenarios under subsidized regimes, but not yet for grid injection applications, suggesting the need for financial incentives in the field. However, it is argued that these systems hold major social and environmental value, enabling different types of business models that might increase stakeholders’ motivation to invest.

Combined 18F-FDG and 11C-acetate positron emission tomography/computed tomography in staging and treatment decision in patients with hepatocellular carcinoma: A cost-effectiveness analysis.

e16176 Background: Dual-tracer positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose and 11C-acetate is increasingly used in the management of hepatocellular carcinoma (HCC). However, considering the high cost of dual-tracer PET/CT, there is a need to assess its value by considering both the efficacy and cost. Methods: In this retrospective cohort study, 223 dual-tracer PET/CT scans were performed in 206 patients between 2014 and 2020 for tumor staging for radiologically or histologically confirmed HCC. New lesion detection rate, changes in the Barcelona Clinic Liver Cancer (BCLC) classification, and changes in treatment allocation based on dual-tracer PET/CT were evaluated. Validation was performed on a cohort of staging patients from another institution (n = 48). A cost-benefit analysis model was built for the incurred costs and the impact of dual-tracer PET/CT findings on treatment strategy over conventional imaging was studied. Results: Of the 223 patients that underwent dual-tracer PET/CT for staging, new lesions were detected in 33 (14.8%) patients, resulting in BCLC upstaging in 26 (11.6%) and treatment modification in 16 (7.2%) cases. The external staging cohort showed similar findings where dual-tracer PET/CT resulted in 12.5% (6/48) BCLC upstaging and 10.4% (5/48) treatment modifications (concordance-index: 0.85; 95% CI: 0.82-0.88). In subgroup analysis of 200 patients performed for pre-treatment metastatic screening, dual-tracer PET/CT detected extra-hepatic metastasis in 11, 10, and 16 patients, respectively, and led to treatment modifications in all of these patients. Sixteen locoregional therapies (8%) were avoided (8 resections, 3 transplantations, and 5 TACEs), with an estimated cost saving of US$161,665 (US$808 / patient) from the use of dual-tracer PET/CT. Sensitivity analyses suggested that dual-tracer PET/CT is consistently more cost-saving than conventional imaging, irrespective of the changes in the cost of imaging, procedures, and the duration of hospital stay (range: US$112,945-210,385). The cost of PET/CT had the greatest influence on the cost savings. Conclusions: Comparing to conventional imaging, dual-tracer PET/CT improves tumor staging, alters treatment allocation and is cost-saving in patients undergoing pre-treatment metastatic screening. Our results demonstrated that the use of dual-tracer PET/CT provides additional value and improves patient care.

An integrated techno-economic analysis on wastewater reclamation in Hong Kong: A comprehensive cost – Benefit analysis with life cycle assessment

Wastewater reclamation and reuse projects have been gaining significant attention as ways to secure sustainable water resources while preventing environmental degradation. However, traditional cost-benefit analyses do not reflect the true net benefits of these processes as they do not consider all externalities, particularly due to the difficulties in monetizing environmental impacts, thereby leading to underutilization. In this study, we conducted a hybrid cost-benefit analysis with an integrated environmental externality approach to evaluate the true net benefit of wastewater treatment and reclamation with explicit and implicit costs and estimated benefits. Firstly, we developed a cost-benefit analysis model based on the Drainage Service Department's statistical data from the Shek Wu Hui plant in Hong Kong from 2014 to 2018. Next, we estimated the external environmental benefits from pollutant abatement based on the shadow price of each pollutant by adopting a distance frontier function. We then conducted a life-cycle assessment to investigate the potential environmental impacts of the process using SimaPro 8.5.2.0 software and the EcoInvent 3 database, utilizing Stepwise2006 methodology for full monetization. A sensitivity analysis was applied to control for uncertainties as a robustness check. Our results show that aggregate cost, shadow price, and monetized environmental impact are estimated to be 4.47, 4.75, and 1.23 HKD per m3 of treated water respectively. Based on these results, our study illustrates that the true benefit is noticeably higher than the unit cost of wastewater reclamation, dependent upon a scale, and represents a breakeven point of 63,769 m3 of water reclamation per day. Our study further demonstrates that wastewater reclamation is viable from both social and economic perspectives and will increase the government budget by 81 million HKD. Therefore, this study can provide a basic guideline for developing a more complete economic valuation of wastewater treatment and reclamation projects.

Cost–benefit analysis to appraise technical mitigation options for earthquake-induced liquefaction disaster events

PurposeRecent earthquake-induced liquefaction events and associated losses have increased researchers’ interest into liquefaction risk reduction interventions. To the best of the authors’ knowledge, there was no scholarly literature related to an economic appraisal of these risk reduction interventions. The purpose of this paper is to investigate the issues in applying cost–benefit analysis (CBA) principles to the evaluation of technical mitigations to reduce earthquake-induced liquefaction risk.Design/methodology/approachCBA has been substantially used for risk mitigation option appraisal for a number of hazard threats. Previous literature in the form of systematic reviews, individual research and case studies, together with liquefaction risk and loss modelling literature, was used to develop a theoretical model of CBA for earthquake-induced liquefaction mitigation interventions. The model was tested using a scenario in a two-day workshop.FindingsBecause liquefaction risk reduction techniques are relatively new, there is limited damage modelling and cost data available for use within CBAs. As such end users need to make significant assumptions when linking the results of technical investigations of damage to built-asset performance and probabilistic loss modelling resulting in many potential interventions being not cost-effective for low-impact disasters. This study questions whether a probabilistic approach should really be applied to localised rapid onset events like liquefaction, arguing that a deterministic approach for localised knowledge and context would be a better base for the cost-effectiveness mitigation interventions.Originality/valueThis paper makes an original contribution to literature through a critical review of CBA approaches applied to disaster mitigation interventions. Further, this paper identifies challenges and limitations of applying probabilistic based CBA models to localised rapid onset disaster events where human losses are minimal and historic data is sparse; challenging researchers to develop new deterministic based approaches that use localised knowledge and context to evaluate the cost-effectiveness of mitigation interventions.

Investigating and modeling the dynamics of long ties

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds.

Comparative profitability and relative risk of adopting climate-smart soil practices among farmers. A cost-benefit analysis of six agricultural practices

The adoption of climate-smart soil (CSS) practices among farmers have the potential to rehabilitate and protect the soil. Proponents have not fully addressed factors such as; profitability and the relative risk that farmers face during the adoption and implementation of these CSS practices. These factors determine the adoption and sustainability of these practices. This study assessed the comparative profitability and relative risk of implementing CSS practices among farmers in Kakamega, Siaya, and Bungoma counties in Western Kenya. The prioritization of these CSS practices (agroforestry, intercropping, liming, organic manure use, inorganic fertilizer, and improved hybrid seeds) was based on the climate-smart agriculture (CSA) pillars (production, adaptation, and mitigation) and their benefits. A deterministic cost-benefit analysis model that incorporates sensitivity and scenario analysis assessed these factors. The findings showed that agroforestry was the most profitable having a net present value of US$ 16,071 ha−1, followed by intercropping (US$ 10,487 ha−1), and the use of improved hybrid seeds was the least profitable (US$ 881 ha−1). In terms of relative risk, all the practices were more sensitive to the product price and output than the lifespan, discount rate, and labour cost. The result implies that exposure of these practices to climatic and economic shocks will result in high-profit risk. Therefore, national and county governments should place micro-credit loans with minimum interest, input subsidies, and skilled personnel to promote increased adoption of agroforestry and intercropping. Agricultural extension officers should also demystify farmers' mentality that improved hybrid seeds can guarantee increased productivity.