Search Results (17)

Large-scale transitions to eco-friendly vehicle fleets present complex capital budgeting challenges, requiring the integration of extensive operational data with financial modeling while balancing economic profitability and environmental sustainability. Traditional approaches often struggle to manage this complexity and quantify the inherent trade-offs. This study develops and applies an innovative integrated accounting-and-finance framework to evaluate the economic and environmental implications of green fleet transition projects, explicitly quantifying the trade-off between profitability and sustainability. Focusing on waste vehicle replacement of Iren Spa, a leading European multi-utility company, we employ the recently developed Split-Screen Approach, a unified accounting-and-finance framework grounded in the laws of motion and conservation. It automatically reconciles pro forma financial statements and generates internally consistent valuation metrics, eliminating the manual adjustments and inconsistencies of traditional models. Its built-in diagnostic checks and scalability for highly complex datasets overcome the manual adjustments and inconsistencies inherent in traditional financial models. We process 2303 inputs across multiple “green” scenarios. This methodology integrates an Engineering Model, describing fleet evolution, operating costs, and CO₂ reduction, with a HookUp Model, which serves to transform scenarios into well-defined projects. The latter model is then integrated with a Financial Model that generates pro forma financial statements, incorporates financing and payout policies, and assesses economic profitability through Net Present Value (NPV) and consistent accounting rates of return. Together, these elements form a robust framework for managing complex data integration and analysis. Our research reveals a fundamental trade-off: enhanced environmental sustainability (measured by Net Green Value, NGV), which quantifies CO₂ reduction, is achieved at the expense of economic profitability, measured by NPV. This financial sacrifice is captured by the Net Value Curve, a Pareto frontier, while the NPV-to-NGV ratio provides “shadow prices” for CO₂ reduction, revealing the financial cost per unit of sustainability gained. Based on 21 project scenarios and additional sensitivity analyses on financial inputs and energy prices, the results confirm a decreasing relationship between NGV and NPV. This study makes three main contributions: (1) it demonstrates the practical application of the Split- Screen Approach for capital budgeting under complexity, (2) it introduces the Net Value Curve framework as a useful tool for visualizing and quantifying the trade-off between profitability and sustainability, (3) it provides managers and policymakers actionable insights, supporting more informed decisions in green fleet transition planning where economic and environmental objectives may conflict. The findings provide managers and policymakers with a rigorous and transparent accounting-and-finance framework that enhances the reliability of capital budgeting decisions compared with traditional financial modeling, while offering a Paretian frontier for evaluating environmental trade-offs. Full article

Before the development of 3D cadastre, cadastral systems were based on 2D representations, which now require transformation or updating. In this context, the first issue is that existing 2D rights are not aligned with recent 3D data acquired using advanced technologies such as Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR). The second issue is that point clouds of objects captured by UAV-LiDAR, such as fences and exterior building walls—are often neglected. However, these point cloud objects can be utilized to adjust 2D rights to correspond with recent 3D data and to update 3D building models with a higher level of detail. This research leverages such point cloud objects to automatically generate 3D rights and building models. By combining several algorithms, such as Iterative Closest Point (ICP), Random Forest (RF), Gaussian Mixture Model (GMM), Region Growing, the Polyfit method, and the orthogonality concept—an automatic workflow for generating 3D cadastral models is developed. The proposed workflow improves the horizontal accuracy of the updated 2D parcels from 1.19 m to 0.612 m. The floor area of the 3D models improves by approximately ±3 m². Furthermore, the resulting 3D building models provide approximately 43% to 57% of the elements required for 3D property valuation. The case study of this research is in Indonesia. Full article

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability. Full article

While the use of machine learning (ML) in automated real estate valuation is growing, research on stacking ML models into ensembles remains limited. In this paper, we propose a stacked ensemble model for valuing residential properties. By applying our models to a comprehensive dataset of residential real estate transactions from Jeju Island, spanning 2012 to 2021, we demonstrate that the predictive power of ML-based models can be enhanced. Our findings indicate that the stacked ensemble model, which combines predictions using ridge regression, outperforms all individual algorithms across multiple metrics. This model not only minimizes prediction errors but also provides the most stable and consistent results, as evidenced by the lowest standard deviation in both absolute errors and absolute percentage errors. Additionally, we employed the decision tree method to analyze the conditions under which specific features yield more accurate results or less reliable outcomes. It was observed that both the size and age of an apartment significantly impact prediction performance, with smaller and older complexes exhibiting lower accuracy and higher error rates. Full article

The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction. Full article

Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the relationships between public schools and the various submarkets of Greater Sydney. This is valuable for the decision-making processes of home buyers, developers and policymakers. Full article

An important and challenging question for airport operators is the management of airport capacity and demand. Airport capacity depends on the available infrastructure, external factors, and operating procedures. Investments in Air Traffic Management (ATM) infrastructures mainly affect airside operations and include operational enhancements to improve the efficiency, reliability, and sustainability of airport operations. Therefore, they help increase capacity while limiting the impact on the airport infrastructure itself. By reviewing the neoclassical valuation principles for Cost–Benefit Analysis (CBA), we find that it does not consider relevant behavioral economic challenges to conventional analysis, particularly: failure of the expected utility hypotheses, dependence of valuations on reference points, and time inconsistency. These challenges are then incorporated through practical guidelines into the traditional welfare model to achieve a new methodology. We propose a novel CBA behavioral framework for investments in ATM infrastructures to help policy makers and airport operators when faced with a capacity development decision. This is complemented with a practical example to illustrate and test the applicability of the proposed model. The case study evaluates the deployment of Automatic Dependent Surveillance–Broadcast (ADS–B) as an investment aimed at improving ATM operational procedures in the airport environment by providing advanced ground surveillance data. This allows airport operators to discover the causes of taxi congestion and safety hotspots on the airport airside. The benefits of ADS–B are related to enhanced flight efficiency, reduced environmental impact, increased airport throughput, and improved operational predictability and flexibility, thus reducing waiting times. At the airport level, reducing the waiting times of aircraft on the ground would lead to a capacity release and a reduction in delays. The results show that, following a traditional CBA, the investment is clearly viable, with a strong economic return. Including behavioral notions allows us to propose a new evaluation framework that complements this conclusion with a model that also considers inconsistencies in time and risk perception. A positive Net Present Value can turn into a negative prospect valuation, if diminishing sensitivity and loss aversion are considered. This explains the reticent behavior of decision makers toward projects that require robust investments in the short-term, yet are slow to generate positive cash flows. Finally, we draw conclusions to inform policy makers about the effects of adopting a behavioral approach when evaluating ATM investments. Full article

Spatial conservation prioritization does not necessarily lead to effective conservation plans, and good plans do not necessarily lead to action. These “science-action” gaps are pernicious and need to be narrowed, especially if the international goal of conserving 30% of the planet by 2030 is to be realized. We present the Earthwise Framework, a flexible and customizable spatial decision support system (SDSS) architecture and social process to address the challenges of these science-action gaps. Utilizing case study experience from regions within California, South Africa, and British Columbia, we outline the framework and provide the Little Karoo, South Africa SDSS data, code and results to illustrate five design strategies of the framework. The first is to employ an “open science” strategy for collaborative conservation planning and action. Another is that marginal value functions allow for the continuous accounting of element (e.g., habitat) representation in prioritization algorithms, allowing for an SDSS that is more automated and saves valuable time for stakeholders and scientists. Thirdly, we program connectivity modeling integrated within the SDSS, with an algorithm that not only automatically calculates all the least cost corridors of a region, but prioritizes among them and removes the ones that do not make ecological sense. Fourth, we highlight innovations in multi-criteria decision analysis that allow for both cost-efficient plan development, like representative solution sets, but also land-use planning requirements, like site specific valuation, in what appears to be a more transparent, understandable, and usable manner than traditional approaches. Finally, strategic attention to communicating uncertainty is also advocated. The Earthwise Framework is an open science endeavor that can be implemented via a variety of software tools and languages, has several frontiers for further research and development, and shows promise in finding a better way to meet the needs of both humans and biodiversity. Full article

The highway network has to grow because of the increasing vehicle use, the effort to improve road safety, and the needs generated by economic development and efficient international transport. The negative impact of the motorway on agricultural holdings in its vicinity can be determined with general agricultural land valuation methods. However, this approach necessitates an in-depth analysis of land cultivated by each farm, which is rather labour-intensive. Impact on agricultural land should be assessed after the detailed plans for constructing a motorway are ready or even after construction. Nevertheless, simplified methods can be applied as early as the preliminary design stage or when evaluating potential alternative routes. Less labour-intensive, these methods can determine the harmful impact of a motorway on agricultural land with sufficient accuracy. The simplified and automated method presented for assessing the impact of a motorway on agricultural land uses GML files to automatically acquire data for the calculations, using the linear nature of the motorway. The prepared input data is then processed to optimally place the motorway in space. The final step is the visualisation of the road investment. The process has been automated to facilitate rapid analysis and employment of the data in linear project modelling and assessments of available options. Full article

This paper aims to evaluate the degree of affinity that Nordic companies’ reports published under the Global Reporting Initiatives (GRI) framework have. Several natural language processing and text-mining techniques were implemented and tested to achieve this goal. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for word representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a substantial impact on the environment. Full article

The article presents the results of the analysis of the legal and practical aspects of the implementation of land value maps for land consolidation in four countries: Slovakia, Croatia, Poland and Turkey. The discussion indicated that it is not possible at present to construct fully universal methods of automatic earth valuation for LC. The reason is that there are too many different approaches to land value mapping. Identification of areas with similar characteristics (valuation factors) needs to be conducted prior to valuation of individual parcels. In both cases, the agronomic value from the farmer’s point of view is the key valuation criterion. It was pointed out that achieving versatility of algorithms can occur only as a result of extensive parameterisation of the developed models, both in terms of the number of factors considered, as well as the manner and strength of their interaction. The development directions of land valuation mass methods should proceed with the widest possible scope of public participation determining the principles of this valuation, which increases the level of acceptance of both the result of the land valuation itself and the subsequent effects of the land consolidation project. Full article

A common response in public pension systems to population ageing is to link pensions to observed longevity. This creates an automatic stabiliser that arises from the valuation of a private actuarially funded system. However, no private pension plan mechanism has been articulated to adapt to this ageing in relation to the increased costs it entails. Private pension plans focus on saving for retirement; capital is accumulated to pay for it. However, perceptions of health status change over time and, as retirement age approaches, concerns about long-term care (LTC) increase. Moreover, there is not enough time to plan for it sufficiently in advance. This paper proposes to incorporate a mechanism to add an allowance to the financial pension (retirement, disability, rotation) to cover LTC within a private defined benefit pension plan, in the case of a pensioner becoming dependent. Depending on a pensioner’s health status, both the expected number of payments and their intensity are transformed. For this purpose, a mechanism is defined (through Markov chains) to adapt the amount of LTC support to a beneficiary’s health-related life expectancy. The study’s main contribution is that it establishes a private pension plan model that offers to incorporate dependency aid through this mechanism into the economic pensions without increasing the total cost of the plan. It adapts to life expectancy according to a person’s state (healthy, disabled, dependent). Full article

The principle behind sustainable city movements is represented by the idea of “good living”, which is the possibility of having solutions and services that allow citizens to live in an easy, simple, and enjoyable way. Policies for urban quality play a central role in the slow cities manifesto, often suggesting the use of Information and Communication Technologies (ITC) in the development of interactive services for citizens. Among these, an interesting possibility is to offer citizens digital real estate consultancy services through the implementation of automated evaluation methods. An automated appraisal action—which is already complex in itself owing to the need to collect data in a consistent, standardized, but also differentiated way so as to require the adoption of real estate due diligence—collides on the operational level with the concrete difficulty of acquiring necessary data, much more so since the reference market is dark, atypical, and viscous. These operational difficulties are deepened by the epistemological nature of the appraisal discipline itself, which bases its methodology on the forecast postulate, recalling the need to objectify as much as possible the evaluation from the perspective of an intersubjective sharing argument. These circumstances have led, on the one hand, to the definition of internationally accepted uniform evaluation rules (IVS, 2017) and, on the other, to the testing of automated valuation methods aimed at returning computer-based appraisals (AVM). Starting from the awareness that real estate valuation refers essentially to information and georeferences, this paper aims to demonstrate how real estate appraisal analysis can be further improved through information technology (IT), directing real estate valuation towards objectivity in compliance with international valuation standards. Particularly, the paper intends to show the potential of combining geographic information systems (GISs) and building information models (BIMs) in automated valuation methods through the depreciated reproduction cost. The paper also proposes a BIM-GIS semi-automatic prototype based on the depreciated reconstruction cost through an experimentation in Rende (Italy). Full article

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment. Full article

Interest rates are representative indicators that reflect the degree of economic activity. The yield curve, which combines government bond interest rates by maturity, fluctuates to reflect various macroeconomic factors. Central bank monetary policy is one of the significant factors influencing interest rate markets. Generally, when the economy slows down, the central bank tries to stimulate the economy by lowering the policy rate to establish an environment in which companies and individuals can easily raise funds. In Japan, the shape of the yield curve has changed significantly in recent years following major changes in monetary policy. Therefore, an increasing need exists for a model that can flexibly respond to the various shapes of yield curves. In this research, we construct a three-factor model to represent the Japanese yield curve using the machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the “Level,” “Curvature,” and “Slope” of the yield curve. Furthermore, we develop a long–short strategy for Japanese government bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend-follow investment strategy. Full article