1 Introduction

Sustainable development is a global agenda that aims to ensure economic growth while also protecting the environment and promoting social well-being. Companies have a critical role to play in achieving these goals (Rajesh 2020 and Qureshi et al. 2020), particularly by means of effective budgeting (Raper et al., 2022). By allocating resources strategically and responsibly, companies can ensure that their operations are sustainable (Giannetti et al 2020; Tsani et al 2020). However, for this to happen, companies need to be aware of specific SGs which are relevant to their operations, and then develop budgeting strategies that encompass these goals (Martínez-Córdoba et al., 2023). This is the case of a Spanish utility company providing water services, distribution and treatment, —henceforth referred to as WSC (Water Supply Company)— which, after an internal reflection process, recently published a strategic action plan. This initiative reflects the WSC’s wish to strengthen its position as a global reference in integral water cycle management while contributing to sustainable development in the urban environment. Consequently, it becomes imperative to procure models which guide decision-makers to allocate resources sustainably, strategically and conscientiously (Sabale et al. 2023; Tsakiris et al., 2023). These models should make it easier to prioritize investments in assets which align with predefined resilient, sustainable criteria, all within the constraints of a finite budget (Roige 2020).

Private sector decision-making models commonly rely on cost-effectiveness analysis models, which involve comparing the costs of various homogeneous alternatives for the same type of asset. Furthermore, monetary-based decision-making approaches encompass financial analysis and cost–benefit analysis models (Pujadas et al. 2017 and Bakopoulou et al 2007). Nevertheless, true sustainability hinges on simultaneous interconnection of the economic, environmental, and social dimensions of well-being. Multi-criteria analysis (MCA) emerges as a highly valuable tool for scenarios. Multiple MCA methodologies have been developed to systematically address the multidimensional nature of real-world problems (Hajkowicz and Collins 2007 and Kabir et al 2013). However, they have never been used as an effective budget allocation system.

This article focuses on the pivotal significance of water companies' budget allocations synchronized with meeting Sustainable Goals. Furthermore, it provides an example of how WSC's financial allocations were harmonized with SGs, using a multicriteria decision-making model. The significance and novelty of this research lies in its ability to provide decision-makers and stakeholders with valuable insights into the investment allocation process, respecting both transparency and traceability. By highlighting the pivotal indicators and their respective roles in guiding investments, this study facilitates informed decision-making and alignment of financial resources with specific objectives. Moreover, our analysis fosters a deeper understanding of the dynamics of investment allocation in various scenarios.

2 Scope of Study and System Boundaries

Within the Budget Allocation Model, this paper proposes to examine the potable water supply system of WSC —a Spanish utility company providing water services, distribution and treatment. This system encapsulates the foundational phases of the all-encompassing water cycle, including collection, purification, conveyance, storage, distribution, and consumption. For this purpose, WSC has outlined the following relationship between budget items and sub-items:

  • Item 1 (PRODUCTION) includes sub-items: Network Expansion; Network Renewal; Expansion of Facilities and Mechanisms; Renovation of Facilities and Mechanisms; Expansion Treatment; Renewal Treatment; and Operational Control

  • Item 2 (TRANSPORT) includes sub-items: Network Adaptation and Renewal; Adaptation and Renovation of Transport Facilities and Mechanisms; Network Expansion; and Expansion of Facilities and Mechanisms

  • Item 3 (DISTRIBUTION) includes sub-items: Expansion and Reinforcement of the Network, Installations and Distribution Mechanisms; Distribution Performance Improvement Plan; Renovation of Valves, Mechanisms and Dataloggers; Enlargement Connections; and Renewal Connections; Renewal of the Distribution Network

  • Item 4 (REMAINING AREAS), considered as a whole and is not divided into sub-items, although this includes: Building Management, Operational Control, Laboratory, Information Systems, Supply, and Alternative Water Resources.

Simplifying the procedural intricacies, the Budget Allocation Model in this manuscript seeks to rationalize the differential augmentation of sustainable development by investing a single unit of currency across varied assets (e.g., decanters, software) pursuing diverse objectives (e.g., improved performance, quality control) and involving various execution strategies (e.g., renewal, expansion). A multi-criteria model such as MIVES (Boix-Cots et al 2022), revised and tailored explicitly for this investigation, was chosen to address this challenge. The ensuing sections comprehensively present the three essential phases of budget allocation: Phase I – Legal Constraints, Phase II – Decision Tree, and Phase III – Sub-Item Matrices (Fig. 1).

Fig. 1
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Phase diagram of the investment prioritization model methodology by item

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3 Methodology

3.1 Phase I – Legal Conditions

The entire investment volume does not have to be distributed using sustainable development criteria, since a small part of the annual investment plan is devoted to unavoidable legal conditions. Consequently, this phase assesses prevailing legal conditions and the corresponding investment requisites. It is imperative to include this phase due to the nature of these investments, stemming from needs identified by governing bodies. It would be incongruous to evaluate the contribution to sustainable development by investments which are not rooted in optimal supply management.

3.2 Phase II – MIVES Decision Tree

The underlying objective of the MIVES decision tree is to methodically, traceably and transparently structure and distribute the budget allocation of each item component. Such allocation is based on the SG contribution index (an index measuring how much a single budget unit invested in a specific item contributes to the sustainable development of WSC).

3.2.1 MIVES Approach

MIVES is a multi-criteria methodology originally developed to assess sustainability in construction (Aguado et al. 2012) and prioritize alternatives (Pujadas et al. 2017; Tan et al. 2024). This methodology provides rational, sustainability-based reasoning for the decision criteria (Boix-Cots et al 2022) and allows a multi-stakeholder approach to reconcile the interests of different stakeholders (Boix-Cots et al., 2023b), structuring the problem within a multi-criteria analysis framework in which different alternatives may be prioritized according to pre-established criteria, to satisfy a pre-defined sustainable objective. A 3-level MIVES framework is developed here to set the pre-established criteria. The three levels range from most general to most specific: dimensions, criteria and indicators. From the three levels of the framework analysis, indicators are the only concepts evaluated during the prioritization process. To integrate all indicators into a final result, decision makers assign a global weight and a value function for each indicator.

3.2.2 Decision Framework

In an endeavour to meticulously configure the entire decision-tree framework to seamlessly align with the conceptual underpinning of Sustainable Development within the purview of WSC's stakeholders, a comprehensive consultation process took place in various seminars. The economic, environmental and social repercussion of each investment will be considered here to encompass all these agendas. The coherence, representativeness, and objectivity of the criteria and indicators considered in each dimension will guarantee the goodness and credibility of its results. With this aim in mind, the most significant and discriminatory indicators were considered, drawing on the knowledge and expertise of WSC authorities. These indicators were meticulously selected to meet the essential attribute criteria outlined by Keeney and Raiffa (1993) for a decision-making system: they must be complete, operational, decomposable, non-redundant, minimal, discriminatory, and comprehensive. Table 2 shows the detailed decision framework list, comprising the 3 aforementioned dimensions, 8 criteria, and 10 indicators.

Social Dimension (D1)

The social dimension framework (D1) encompasses three distinct criteria and a total of seven indicators. The People (customers) criterion (C1) encompasses indicators directly influencing individuals, including Supply Assurance (I1), Service Perception (I2), and Provision Efficiency (I3). The City criterion (C2) considers the urban impact through: Impact on Mobility (I5). Meanwhile, the Workers criterion (C3) encapsulates indicators safeguarding the welfare of supply domain employees, notably the Occupational Health and Safety indicator (I6).

The indicator of effects on Supply Assurance (I1) is comprised of two sub-indicators that capture the aforementioned objectives: risk resilience (\(Ris{c}_{Res}\)), understood as the system's ability to absorb disturbances and reorganize itself while undergoing changes to essentially retain the same function, structure, and feedback, and risk service continuity (\(Ris{c}_{Cont}\)), understood as the potential for service reliability improvement through investment. Ultimately, this indicator (I1) is calculated using the average between the maximum and the mean of resilience risk and service continuity risk, as indicated in Eq. 1.

$$I1=\frac{\mathit{max}\left({Risc}_{Res};{Risc}_{Cont}\right)+\frac{{Risc}_{Res}+{Risc}_{Cont}}{2}}{2}$$
(1)

The Service Perception (I2) depends on various factors, not only the target quality or the perceived quality of the delivered product (in this case, water), but also others such as customer service quality, service price or social actions. Typically, service perception is gauged through surveys answered by users or individuals receiving or affected by the service.

The Provision Efficiency (I3) encompasses any measures which exceed the requirements of current regulations, seeking excellence and task optimization to deliver service as efficiently as possible. Examples might include maintenance planning (preventive maintenance) to avoid sudden breakdowns (corrective maintenance), automatic meter reading and billing, or remote monitoring of equipment.

Regarding the Impact on Mobility (I4), WSC’s business is fully integrated within the cities it serves. This city-business interrelation is particularly significant for the distribution network assets, as it covers almost all streets within the cities. Thus, the investment category associated with a larger set of network sections along streets with heavy traffic, which simultaneously have a greater impact scope in case of failure, will be the category with the highest value of the mobility impact indicator.

Occupational Health and Safety (I5) aims to provide all necessary tools and measures to prevent work-related risks by assessing the potential for action through investing in these facilities. Only unresolved risks specific to each investment category have been considered when calculating I5, determining which could be addressed through investment. Therefore, for each evaluation, the number of risks identified for each classification level (slight, low or tolerable, moderate, significant, intolerable) has been tallied and weighted by the lower limit of the degree of hazard defined according to Fine et al. (1971) methodology. Finally, applying Eq. 2 obtains an I5 risk value for each domain.

$$I5=1\cdot N{Risk}_{slight}+60\cdot N{Risk}_{low}+160\cdot N{Risk}_{moderate}+250\cdot N{Risk}_{significant}+400\cdot N{R}_{intolerable}$$
(2)

Environmental Dimension (D2)

The pervasive threat of climate change is one of the fundamental challenges facing contemporary society. Hence, indicators within this dimension encompass inputs (water and energy, denoted as I6 and I7, respectively) and outputs (emissions, I8). Moreover, they assess the business’s environmental impact, including biodiversity (I9), thereby providing a holistic view of its ecological footprint.

The Efficient Water Consumption indicator (I6) aims to assess the water-saving potential at the source of each category. It achieves this through assessing efficiency improvement capacity, identifying areas with greater potential for hydraulic performance enhancement through investment.

The aim of the Efficient Energy Consumption indicator (I7) is to evaluate the energy recovery potential of each category, enabling analysis of areas with greater improvement capacity through investment in energy consumption. The assets within the supply system that consume energy are situated in Production, Transport, Distribution, as well as in Other Domains, particularly concentrated in the Building Management category.

The CO2 Footprint indicator (I8) is based on greenhouse gas emissions calculation, following the Practical Guide for the Calculation of Greenhouse Gas Emissions (Catalan Climate Change Office 2023). The carbon footprint measures the quality of greenhouse gas emissions associated with a product or an activity. Categories of GHG emissions linked to an organization's activities can be classified as follows: (a) direct emissions, which are emissions from sources owned or controlled by the entity generating the activity; (b) indirect emissions, which are emissions resulting from activities performed by the entity although coming from sources owned or controlled by another entity.

Finally, the biodiversity indicator (I9) measures the degree of opportunity presented by each of the company's installations to promote biodiversity in the environment by creating natural spaces in areas where human activity has built hard surfaces. The biodiversity strategy includes several possible actions: restoration of key water cycle ecosystems, education and awareness, prevention and control of threats and impacts, enhancement of ecosystem services in urban areas, naturalization of infrastructure.

Economic Dimension (D3)

The economic dimension comprises a single indicator, the economic index (I10) intended to ensure economic sustainability from the perspective of optimal renewal of existing assets, while also acknowledging the need for actions that will enhance the supply system and consequently the economic performance of the activity.

Hence, deciding whether to invest in a specific category involves ascertaining the degree of obsolescence of the assets within it and the financial return generated by the investments made therein. Consequently, the economic index is calculated as the average of the maximum and the mean of the Economical Risk (\(Ris{c}_{Eco}\), calculated for each category as the product of the economic depreciation relative to the average of that category, and multiplied by the theoretical annual investment percentage of that category, and Economical Opportunity (\(Opo{r}_{Eco}\)), calculated by means of the Return On Assets, known as ROA, as indicated in Eq. 3.

$$I10=\frac{\text{max}\left(Ris{c}_{Eco};Opo{r}_{Eco}\right)+\frac{Ris{c}_{Eco}+Opo{r}_{Eco} }{2}}{2}$$
(3)

Employing the maximum and the mean to derive a representative value of the financial contribution made by each category serves the purpose of automating the criterion shift that would occur in practice for categories with maximum deviation between the two indicators (risk and opportunity). With this approach, for instance, a higher potential economic contribution is attributed to categories with low ROA but which present a substantial need for renewal. In other words, this applies to categories with a significantly high relative risk, especially concerning the opportunity value.

The specific details regarding how to construct the indicators, including data sources and methodologies, can be found in the doctoral thesis by Roigé N. (2020) —first author of this manuscript.

3.2.3 Value Functions

According to research by Alarcón et al. (2010), a value function is proposed for each indicator. This function transforms evaluations into numerical values ranging from 0 to 1, making it possible to determine equivalences between different units of the indicators. These numerical values represent the minimum and maximum satisfaction levels of decision-makers, respectively. The satisfaction criteria for decision-making associated with each indicator in this study can be adequately represented using decreasing (D) or increasing (I) functions, which include linear (Lr), concave (Ce), convex (Cx), or S-shaped (S) functions. Appendix A provides full details on the value function construction. Accordingly, Table 1 shows the data and the form of each value function.

Table 1 Value Functions
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Note that the Biodiversity indicator is the only one that does not use the classic value function to transform the value of the indicator into a dimensionless scale from 0 to 1. A discrete piecewise function is used for this purpose, where values ranging from 0 to 9 receive a 0, values ranging from 10 to 18 receive a 1/3, values ranging from 19 to 27 receive a 2/3, and values ranging from 28 to 81 receive a 1.

3.2.4 Weights

In this case, an effort was made to accommodate all WSC stakeholders' interests by defining weights using the Analytic Hierarchy Process (AHP) approach. The AHP, originally developed by Saaty (1980), is a linear additive model that converts subjective assessments of relative importance into overall scores or weights through pairwise comparisons between criteria and options (full details on the AHP process in MIVES applications can be found in Pujadas et al. (2017). These pairwise comparisons are determined during seminars by decision-makers using AHP, reflecting the relative importance of each dimension, criterion, and indicator for prioritization based on their knowledge, expertise, and preferences. It is important to note that the weights presented in Table 2 represent the decisions taken by WSC decision-makers thanks to expert analysis during these seminars. However, it is crucial to understand that these weights are not fixed and may vary, depending on the user or decision-maker. Nevertheless, the values provided in Table 2 serve as a benchmark for the manuscript (later revealed as scenario 1 when presenting the results and conducting a sensitivity analysis).

Table 2 Decision tree framework
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3.2.5 Contribution Index by Budget Item

The global weights of the indicators are calculated by multiplying the local weight of the indicator (wI) by the local weights of the corresponding criterion (wC) and dimension (wD) according to the MIVES method. Thus, SG Contribution index by items, corresponding to the final contribution of each item, is the sum of multiplying all the indicator values by their global weight.

3.3 Phase III – Sub-Item Matrices

After transparent and traceable distribution of the total available budget between all items in accordance with the SGs, the final phase, Phase III, entails allocating the proportional share of the budget to the sub-items nested within each investment category (see Sect. 2).

Making this nuanced allocation at sub-item level requires a discerning evaluation by domain experts, responsible for each respective area, concerning the extent of the contribution rendered by the subheadings to each individual indicator. This evaluative process is facilitated through a qualitative calibrated scale ranging from 1 to 5, wherein 0 signifies a lack of contribution, while 5 signifies peak contribution. Particular attention should be paid to the contribution matrix formula in relation to the economic indicator, which diverges from the conventional scale of contribution levels. Instead, it is contingent upon the percentage derived from the average historical budget allocated over the company’s last three investment plans.

The qualitative assessment of how much the Production, Transport, and Distribution sub-items contribute to each of the indicators is presented in Table 3 (note that Item 4 —Remaining areas, is not divided into sub-items). Using these values, the partial budget of each indicator is distributed for each item weighted by how much the sub-items contribute to that indicator. For example, if the Supply Assurance indicator contributes €100,000 to the Transportation item (value from phase II), this €100,000 will be divided into 16 parts (3 + 3 + 5 + 5 see Table 3). The renovation sub-items (Network Renewal and Renovation facilities) will take 3/16 parts of this budget each and the expansion items will take 5/16 parts of the budget each, and so on for all indicators.

Table 3 Contribution matrix of the Transport subheadings (PI2020 version)
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4 Case Study

This section delves into four different scenarios to unravel the intricate interplay between various indices, ranging from economic considerations to social and environmental impacts. Examining these scenarios (presented in Table 4) provides valuable insights into the various factors that underpin effective decision-making for urban service provision.

Table 4 Local and Global Weights as a % used in the different participation scenarios
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In Scenario 1, economic factors have the highest weight (50%), with attention to supply assurance, mobility, and efficient water use. Traditional service elements are emphasized, such as guaranteed supply and prudent water usage. These weights align with WSC experts' selections in Table 2. Scenario 2 prioritizes mobility, followed by economics and various environmental factors, emphasizing eco-social significance. Scenario 3 maintains the economic and mobility focuses, giving increasing importance to water efficiency and reflecting a balanced approach to social, environmental, and economic concerns. Scenario 4 accentuates non-economic factors such as supply assurance, mobility, and efficient resource usage, stressing broader implications of inadequate infrastructure maintenance on service provision.

4.1 Budget Item Allocation

The investment allocation presented below was derived from an investment volume of 40 million euro (M€) across all scenarios. Analysis compared the outcomes against the average historical budgets from the Past Three Investment Plans (P3IP), which have been proportionally rescaled to align them with an annual investment of 40 M€ considered here for the purpose of comparability in this exercise.

For each scenario, Table 5 marks out allocation of investments across all the indicators corresponding to each budget item. This granular examination is significant as it reveals the pivotal indicators which attract the highest volume of investment for each item. These insights serve as a valuable reference point, equipping decision-makers and managers with essential information to guide their investment choices, ensuring that the allocated funds are strategically directed toward the appropriate investment types for each budget item.

Table 5 Allocation of investments across all the indicators corresponding to each budget item for each scenario (units k€)
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When juxtaposing the outcomes of significant investment items and sub-items with historical P3IP benchmark (Production: €4.70 M; Transport: €11.37 M; Distribution: €19.55 M and Remaining Areas: €4.37 M), comparative analysis reveals distinctive patterns.

In Scenario 1, historical allocations are mirrored, especially with increased investment in Transportation due to acknowledged deficits. Scenario 2 prioritizes social and environmental aspects, favouring Production and Remaining Areas. Scenario 3 balances investments in Production and Transportation while reducing Distribution. Conversely, Scenario 4 significantly augments Transportation investments, reduces Distribution, and makes modest adjustments elsewhere, indicating a departure from historical trends. All scenarios highlight varying emphasis on supply assurance, mobility, efficient resource consumption, and economic indicators, reflecting how the company’s priorities are evolving toward sustainability and operational efficiency.

Further detail of Table 5 provides an analysis of investment allocation across different categories in the context of water management, focusing on how investments impact various indicators.

Investments in the Production category impact Supply Assurance, Efficient Energy Consumption, CO2 Footprint, and Biodiversity, but not Service Perception or Provision Efficiency due to location and production sector limitations. Transport investments target Supply Assurance, Impact on Mobility, and pipeline efficiency. Distribution investments prioritize Supply Assurance, Mobility, and network efficiency. Remaining Area investments focus on Service Perception, Provision Efficiency, water usage, and emissions, with buildings offering potential for biodiversity enhancement. However, Supply Assurance, Impact on Mobility, and Biodiversity indicators receive negligible attention in this category due to limited observable improvements.

4.2 Sub-item Budget Allocation

Allocation of Production sub-items is presented in Fig. 2a. The reader may remember from Sect. 2 that the different sub-items included into the Production item were: Network Expansion, Network Renewal, Expansion of Facilities and Mechanisms, Renovation of Facilities and Mechanisms, Expansion Treatment, and Operational Control. Note that Fig. 2a does not include historical data from the previous Investment Plans as there is no reference available to determine historical data for the subcategories presented here.

Fig. 2
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(a) Production sub-item sensitivity study; (b) Transport sub-item sensitivity study; (c) Distribution sub-item sensitivity study and (d) Fig. 2d. Remaining Areas sub-item sensitivity study

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Investment results remain consistent across all scenarios for Network Expansion, Renewal, and Operational Control. Variability is notable in Renovation of Facilities (ranging from €1.15 M to €4.71 M) and Renewal Treatment (ranging from €0.78 M to €1.77 M) subcategories. Renovation covers biodiversity-related investments, explaining the highest investment in scenario 2. Renewal Treatment sees increased investment in scenarios emphasizing Supply Assurance, environmental, and economic factors. These indicators peak in scenario 2 and dip in scenario 1, with comparable levels in scenarios 3 and 4. This highlights the nuanced prioritization of investments across scenarios, with scenario 2 showing the most pronounced emphasis on biodiversity and renewal treatments.

Figure 2b illustrates the allocation to Transportation, encompassing Network Adaptation and Renewal, and Expansion of Transport Facilities and Mechanisms. All scenarios advocate increased investment in Network Adaptation and Renewal to address existing deficits, despite territorial constraints. The increase in investment in this sub-item ranges from 8 to 54%. Scenario 2 stands out, favouring Adaptation and Renewal of Transport Facilities and Mechanisms, which increases by approximately 40%. Notably, all scenarios highlight the need for proactive measures to prevent asset aging and associated problems. Scenarios 1, 3, and 4 closely align with historical data, while Scenario 2 diverges, prioritizing facility and mechanism adaptation over expansion initiatives.

Figure 2c displays Distribution sub-items, including Network Expansion, Installations, Performance Improvement, Valve Renovation, and Connection Renewal. Investment increases, €1 million for Distribution Network renovation in Scenarios 1, which represent a 10% variation. Scenarios 1 and 4 show slight upticks (10% variation) in Connection Renewal, lessening investments historically due to network performance improvements nearing maximum potential. Prudent network renewal is vital to prevent performance decline and network obsolescence. Other sub-items demonstrate reduced budgeting, reflecting the maturation of past investments in distribution network performance enhancements. This strategic shift highlights the need for targeted investments to sustain network efficacy while acknowledging the achievements and limits of prior endeavours.

Figure 3c shows the Remaining Areas sub-items, which include Building Management, Operational Control, Laboratory, Information Systems, Supply and alternative Water Resources. Scenario 1 recommends increased investment solely in Operational Control (variation of over 90%), reducing investment in other categories, aligning with Scenario 4 to propose the least investment across cross-sector categories. Scenario 2 prioritizes social and environmental indicators, boosting investment in Building Management (an approximate increase of 200%), Operational Control (an approximate increase of 115%), and Laboratory (an approximate increase of 60%). Information Systems maintain high value despite a slight decrease (1%). Scenario 3 prioritizes Economic Index, Impact on Mobility, and Efficient Water Consumption, advocating more investment in Building Management (an approximate 60% increase) and Operational Control (an approximate 120% increase) while slightly reducing investment in Information Systems (30%) and Supply (63%). Scenario 4 emphasizes Supply Assurance, Efficient Water and Energy Consumption, and Impact on Mobility, advocating increased investment in Operational Control (an approximate 260% increase) while decreasing investment in Building Management and Information Systems (an approximate decrease of 30% and 65%, respectively).

5 Conclusions

This paper presents the pioneering MIVES multicriteria decision-making model, representing a significant advancement in budget allocation methodologies. Developed specifically for a Spanish utility company specializing in water services, distribution, and treatment, this groundbreaking approach marks a significant milestone in the field. This innovative approach aligns financial allocations with the company's vision of sustainable development within its service scope in a transparent and traceable manner. The methodology presented here offers a straightforward, transparent framework, fostering objectivity, traceability, and sustainability in the allocation process.

One of the most relevant and novel aspects is that the method developed here enhances decision-making by empowering relational clusters within the utility company to define the sustainable development concept within their service domain. This authority is exercised by assigning weights in the decision tree, ultimately determining investment quotas for individual items (Phase II). Additionally, calibrating investments among sub-items draws on the insights and expertise of technicians, aided by dedicated contribution matrices designed explicitly for this study to ensure objectivity in the allocation process.

The methodology has proven to be a comprehensive approach, encompassing economic, environmental, and social aspects within the decision-making framework. The case study has yielded highly satisfactory results, demonstrating the model's accuracy, consistency, and repeatability in allocations. Furthermore, the method is adaptable, allowing decision-makers to modify criteria by adjusting weights and associated value functions. Its robustness also makes it readily applicable to other companies seeking a reliable allocation approach. However, final decisions are ultimately made by people, who may opt to make minor adjustments to the results provided by the model to address any of the utility company’s potential endogenous needs regarding asset renewal.