Beyond cost: examining operational water exposure and firm adaptations

Abstract

Introduction:

This study examines how corporations are increasingly using adaptive strategies to face the pressure of rising water resource costs. While prior research often treats water costs as a static input burden, in most contexts, water pricing itself remains administratively determined. In such a scenario, the broader financial pressure associated with water use is often understated.

Methods:

Firm-level panel data and a difference-generalized method of moments (GMM) estimator are employed to capture dynamic adjustment processes, which also help address endogeneity and unobserved heterogeneity.

Results:

The results indicate a significant relationship between operational water cost exposure and firm profitability, with additional evidence of heterogeneity across firms with varying levels of water cost exposure. Firms are found to be heterogeneous in their adaptation measures.

Discussion:

These findings suggest that firms exposed to sustained water cost pressures undertake adaptive responses, such as efficiency improvements and operational adjustments, to offset initial cost effects over time. The analysis interprets water cost exposure as a proxy for broader operational and financial risks, including regulatory and reputational pressures. In this context, it is more than a purely price-based effect. By reconciling static and dynamic perspectives, the study contributes to the literature on water-related financial risk and corporate adaptation. The findings have important implications for managers, policymakers, and investors concerned with firm resilience in water-scarce environments.

1 Introduction

Water scarcity has emerged as a critical constraint for water-intensive industries, particularly the food and beverages sector (FAO, 2020; Mekonnen and Hoekstra, 2016). As one of the largest industrial consumers of freshwater, both in terms of direct process use and indirect use in agricultural supply chains, food firms face increasing exposure to physical water risks, regulatory oversight, and sustainability expectations (UNESCO, 2023; CDP, 2022; Zilberman et al., 2023). These pressures have significant financial implications for firm performance and long-term viability.

In the context of this study, “food firms” refer to industrial enterprises engaged in the processing, manufacturing, and commercialization of agricultural and food products, such as beverages, edible oils, processed foods, and agro-based consumer goods. Such firms are characterized by high operational water dependency in production processes, cleaning, cooling, and supply chain integration (FAO, 2011; Porter and Kramer, 2011). Their water exposure is therefore both operational and strategic, spanning input procurement, processing efficiency, and stakeholder expectations.

While existing research extensively examines water efficiency, footprint reduction, and corporate water stewardship (Hoekstra et al., 2011; Morrison et al., 2009; CDP, 2022), relatively limited attention has been paid to how firms dynamically adjust their financial performance in response to water-related cost pressures. A majority of empirical studies conceptualize water cost narrowly, treating it as a direct input price or utility expense. However, in many regions, particularly emerging and regulated economies, water pricing is often subsidized or administratively determined (Irfeey et al., 2023; Widanage and Chan, 2024; Xiao et al., 2021). This implies that observed water charges may not fully reflect the broader economic burden of water use.

Beyond explicit prices, firms are increasingly exposed to indirect water-related costs, such as regulatory scrutiny, reputational risks, legitimacy concerns, and threats to operational continuity (Rudebeck, 2022; Barbosa and Pumpín, 2024). Therefore, the literature reframes water not merely as an operational input but as a multi-dimensional source of financial exposure. Such exposure influences firms’ investment decisions, capital costs, supply chain stability, and stakeholder perceptions (Zheng et al., 2022, 2024).

Empirical evidence on the financial consequences of water costs remains mixed. Static analyses frequently report a negative relationship between water-related costs and profitability, reflecting the argument that higher resource costs erode firm margins. In contrast, emerging studies suggest that firms facing sustained water pressures may respond through efficiency improvements, technological upgrading, and organizational adjustments to offset cost burdens over time (Nikmaram and Rosentrater, 2019; Lu et al., 2023; Moreno, 2024). Evidence further shows that water efficiency initiatives yield positive financial outcomes primarily when firms possess sufficient operational capability and adaptive capacity (Barbosa and Pumpín, 2024; Rosa et al., 2023). This indicates that static models may fail to capture the dynamic adjustment processes through which firms internalize water cost pressures.

Furthermore, the literature documents a range of financial and operational responses to water-related risks, including financial hedging instruments, water rights trading, cooperative infrastructure investments, and corporate social responsibility (CSR)-driven water stewardship (Larson et al., 2012; Ji et al., 2023; Liu et al., 2024; Hamilton et al., 2024). To date, much of the evidence remains fragmented, often focusing on individual strategies or case-based studies. Empirical analyses of how such responses translate into financial performance remain limited.

This study attempts to address this gap by examining prior literature and empirically examining how food and beverage firms dynamically adjust in response to water–cost exposure. This study builds on a prior work by the authors examining water cost intensity and firm performance in a similar empirical context. However, the present analysis departs conceptually by treating water-related costs as a broader operational and financial exposure rather than a direct input cost (Dasgupta et al., Forthcoming). Accordingly, the focus here is on dynamic firm adaptation and adjustment processes under sustained water-related pressures, rather than static cost-efficiency relationships. It aims to contribute to the literature by providing dynamic empirical evidence on the relationship between water costs and firm performance. It also reconciles conflicting static and dynamic findings and advances the conceptualization of water costs as a multi-dimensional financial exposure encompassing regulatory, reputational, and operational dimensions. In this regard, it offers insights relevant to managers, policymakers, and investors seeking to enhance firm resilience in water-scarce environments.

2 Literature review

The literature over the past two decades emphasizes that water costs faced by food firms are multi-dimensional and extend beyond explicit water prices. In many regions, particularly in emerging economies, water tariffs are administratively determined from a public goods perspective. The prices are generally subsidized, which understates the true economic burden of water use (Irfeey et al., 2023; Widanage and Chan, 2024). Consequently, observed water charges do not fully capture a firm’s total exposure to water-related risks. Recent literature suggests water cost as a broader form of financial exposure, encompassing regulatory scrutiny, reputational risk, legitimacy concerns, and threats to operational continuity (Rudebeck, 2022; Barbosa and Pumpín, 2024).

This exposure marks a departure in literature from emphasis on technical efficiency and water footprint reduction toward a financial perspective. Water scarcity influences capital costs, investment decisions, and long-term firm viability (Zheng et al., 2022, 2024). Empirical evidence suggests that firms facing higher water-related vulnerabilities experience higher financial constraints and a higher cost of capital, even when the direct water prices appear low (Zheng et al., 2024). Therefore, water charges increasingly act as signals of underlying risk rather than merely input costs.

2.1 Management responses to water-related risk

A growing body of literature examines how firms deploy strategies to manage water-related risks. These strategies include sustainability reporting, reputation management, and compliance, among others. Similarly, hedging instruments, water rights trading, innovative derivatives, and blended financial mechanisms (Larson et al., 2012; López-Cabarcos et al., 2024; Bartolini et al., 2024) have also been suggested. Studies demonstrate that weather derivatives and volumetric risk hedging instruments can protect firms against revenue volatility caused by water scarcity (Bartolini et al., 2024), while water rights trading has been shown to enhance corporate productivity via green innovation channels (Liu et al., 2024).

Granata and Di Nunno (2025) advance this perspective by proposing Adaptive Water Infrastructure as a Service (AWIaaS). This integrates AI/IoT-enabled monitoring with green bond financing to modularize resilience investment. Their framework suggests that performance-based contracts could reduce operational water cost exposure (OWCE) by 20–30%.

However, the effectiveness of such financial instruments is highly contingent on firm-level capabilities and institutional context. Shantia et al. (2019) and Xing et al. (2022) highlight that financial hedging alone is insufficient unless complemented by technological improvements and operational adjustments. Similarly, Brears (2023) and Rhouma et al. (2025) note that innovative financial instruments, such as green bonds and blended finance, facilitate water security. Such investments, however, face adoption barriers related to regulatory clarity and market maturity (Egan, 2018; Meng et al., 2022). Jiang (2023) argues that such financial innovations require institutional preconditions, particularly governance reforms in subsidized regimes, where administered pricing distorts cost signals. Overall, the effectiveness of financial instruments depends on regulatory clarity and market maturity.

Some literature articles focus on specific instruments or case studies, offering limited insights into how firms dynamically adjust financial performance over time in response to persistent water cost pressures (Leroux et al., 2022). This gap is particularly important in food industries, where water dependency is structurally high and financial responses are intertwined with operational decisions.

2.2 Operational efficiency and technological adaptation

Operational efficiency emerges as a central moderating factor in translating water management efforts into financial performance gains. Empirical studies consistently show that improvements in water efficiency yield positive financial outcomes. However, realizing these benefits requires adequate operational capabilities (Bulcke et al., 2020). On the other hand, advanced technologies, such as membrane filtration, water reuse frameworks (Asgharnejad, et al., 2021; Pulluru, 2023), and water pinch analysis, significantly reduce water consumption and associated costs (Lima et al., 2021; Bailone et al., 2022), but their financial benefits are unevenly distributed across firms.

Research indicates that firms with higher baseline efficiency, stronger managerial capabilities, and better integration of environmental management systems are more likely to convert water efficiency improvements into profitability gains (Nikmaram and Rosentrater, 2019; Barbosa and Pumpín, 2024). Conversely, firms lacking these capabilities may experience increased capital expenditures without commensurate financial returns. This heterogeneity underscores the importance of dynamic adjustment processes, rather than static cost–performance relationships.

2.3 Cooperative and institutional arrangements

Beyond firm-internal strategies, cooperative arrangements play an increasingly important role in managing water cost pressures. Studies on inter-utility agreements, investment-sharing models, and water rights trading highlight the benefits of risk pooling and economies of scale (Hamilton et al., 2024; Chen and Huang, 2023; Liu et al., 2024). Cooperative investments reduce infrastructure costs and enhance water supply reliability (Aivazidou et al., 2022; Cole et al., 2023; Gorelick et al., 2023; Lima et al., 2021), thereby reducing long-term financial risk for participating firms.

Mallawaarachchi et al. (2012) frame this institutionally: for instance, groundwater is regulated as a “mineral resource,” reallocating scarcity costs via stricter environmental impact assessments (EIA). Such institutional changes amplify operational water costs.

However, cooperative strategies face institutional complexities, including asymmetric risk exposure, governance challenges, and adaptability to changing demand conditions (Hamilton et al., 2024). While cooperation enhances resilience, empirical evidence on direct financial performance effects for food firms remains limited. The literature often treats cooperation as a static arrangement, overlooking how firms adjust participation and financial commitments dynamically over time.

2.4 Regulatory, climate, and legitimacy channels

Regulatory frameworks do exert a deep influence on how firms perceive and respond to water costs. Water pricing reforms, environmental regulations, and water-use licensing shape firms’ financial strategies and operational decisions (Fakoya, 2014; Irfeey et al., 2023). Empirical studies show that regulatory pressure can increase firms’ transformation risks and capital costs (Zheng et al., 2022, 2024). However, targeted government interventions and incentives may mitigate physical water risks and even encourage innovations.

Odunola et al. (2024) integrate climate vulnerability indices with pricing mechanisms, demonstrating that adaptation investments may yield positive net present value under conditions of scarcity. It highlights how climate-adjusted pricing and risk assessments materially alter investment behavior. Thus, the dynamic nature of water cost exposure is being reinforced.

Important to consider in this study is that regulation also amplifies non-price dimensions of water cost. Corporate social responsibility (CSR)-driven water management and regulatory scrutiny influence a firm’s reputation and legitimacy. It shapes investor perceptions and stakeholder relations (Weber and Saunders-Hogberg, 2020; Barbosa and Pumpín, 2024). Studies find that CSR and water stewardship initiatives contribute to reduced water use and improved reputational outcomes (Chen and Huang, 2023; Jones et al., 2015). The literature, therefore, indicates that water cost operates through legitimacy channels in addition to direct financial mechanisms. Firms respond not only to explicit charges but also to anticipated regulatory scrutiny, community pressure, and reputational risk (Rudebeck, 2022; Egan, 2015). These indirect costs are difficult to quantify but play a crucial role in shaping managerial behavior, particularly in water-intensive sectors. From a theoretical perspective, water cost exposure can be interpreted through complementary lenses of transaction cost economics and Institutional theory. Transaction cost economics suggests that firms respond to cost uncertainty and input volatility by reorganizing operations, improving efficiency, and reducing dependencies on unstable resources. In the context of firms more dependent on water variability and unpredictability increase transaction and coordination costs. In a way, it incentivizes firms to adopt efficiency enhancing and cost minimizing adaptations.

Institutional theory further explains firm responses beyond purely economic considerations. Firms operate within regulatory, social and normative environments that impose expectations regarding responsible water use and sustainability practices. Regulatory scrutiny, environmental compliance requirements, and stakeholder pressures create legitimacy-driven incentives for firms. They are effective in firms to direct towards adoption of water efficient practices even when direct cost signals are weak.

These perspectives are important in taking the understanding towards exposure to water-related expenses, not only as an economic constraint but also as an institutional signal

2.5 Fragmentation in existing literature and research gap

Despite these rich insights, the literature remains fragmented. Financial instruments, operational efficiency, cooperative strategies, and regulatory influences are often examined in isolation. Few studies adopt longitudinal approaches capable of capturing dynamic adjustment processes (Zheng et al., 2024; Brears, 2023). Empirical work linking operational water cost exposure to dynamic financial performance is scarce, particularly in food industries and in emerging market contexts where administered prices prevail.

Although theoretically, dynamic capabilities, contingency theory, and resource-based views (RBV) do explain how firms adapt under resource constraints, empirical validation using firm-level data remains limited. This study fills this gap using Indian food firm panel data, validating dynamic capabilities empirically (Granata and Di Nunno, 2025; Jiang, 2023). Operational water cost exposure thus aligns with perspectives that view resource-related costs as signals of constraint and risk that shape firm behavior through adjustment mechanisms rather than as static input prices.

3 Materials and methods

3.1 Hypothesis development

Based on prior research and theoretical underpinnings discussed above, this study examines the influence of water cost exposure on the firm’s performance. From a Transaction cost economic perspective, firms are expected to respond to persistent water cost exposure by reducing cost inefficiencies and improving operational coordination. The literature mentions that water-related costs influence firm outcomes not only contemporaneously but through adjustment processes involving operational efficiency, investment reallocations, and scale responses over time (Zheng et al., 2024; Shantia et al., 2019). Dynamic panel specifications are therefore more appropriate than static models for capturing financial responses to persistent water cost pressures. Therefore, the first hypothesis of this study is as follows:

H1: Water Cost Exposure has a statistically significant dynamic effect on a firm’s financial performance.

While static models often capture the immediate cost burden of water expenses, longitudinal evidence suggests that firms exposed to sustained water-related pressures adopt efficiency-enhancing and risk-mitigating responses that improve financial performance over time (Fu and Jacobs, 2022; Zheng et al., 2022). Under administered pricing regimes, observed water costs may also proxy broader regulatory, reputational, and operational pressures. Simultaneously, institutional theory suggests that these pressures reinforce adaptive responses rather than passive cost absorption. Therefore, the second hypothesis of this study is as follows:

H2: After accepting dynamic adjustment, water cost exposure is positively associated with firm financial performance.

Prior studies highlight heterogeneity in firms’ ability to translate water management efforts into financial gains, driven by differences in operational efficiency, scale, and institutional exposure (Brears, 2023; Barbosa and Pumpín, 2024; Gain et al., 2015). Firms facing higher water cost exposure are more likely to internalize water-related risks and undertake adaptive financial and operational adjustments. Therefore, the third hypothesis of this study is as follows:

H3: The positive dynamic relationship between water cost exposure and financial performance is stronger among firms with higher exposure to water costs.

The emerging finance and resilience literature reframes water cost exposure as a catalyst for adaptation.

3.2 Data and sample

This study employs an unbalanced panel dataset of large firms operating in the food and beverage sector from 2014 to 2023 across multiple Indian states. Many firms operate as multi-plant structures, with facilities located in different water governance jurisdictions. The unit of analysis is the firm-year, and the data are annual.

Firm-level financial data, including profitability indicators and cost components, were obtained from audited financial statements compiled by CMIE. Water-related cost data were drawn from audited financial disclosures and reflect payments made to municipal or state-level water utilities. The majority of firms purchase water through public utilities, state municipal or state-level water authorities, water tankers, and other sources. In addition, some locations rely on direct groundwater extraction, which typically incurs licensing fees, electricity costs, and regulatory charges.

Rate structures differ across states and may include volumetric tariffs, access charges, or increasing block rates. However, firm-level financial disclosures do not break down these cost components. Therefore, the analysis relies on total reported water expenditure. Although reported water cost may not capture the full environmental or scarcity-adjusted social value of water, firms tend to respond to financial costs recorded in their accounting systems.

Importantly, water cost pressures extend beyond direct utility payments. In water-scarce years, such as drought or prolonged dry seasons, reduced water availability affects agricultural yields and raw material supply chains. This can disrupt input availability for food processors, increase procurement costs, and necessitate irrigation or sourcing adjustments. Such supply-side constraints amplify operational and financial exposure beyond direct water tariffs. Although firm-level data do not permit disaggregation of these indirect impacts, the dynamic modeling framework allows adjustment effects to be captured over time.

Accordingly, this study measures “Operational Water Cost Exposure” (OWCE) using reported water-related operating expenditures relative to total production costs. While reported water expenditure may not reflect scarcity-adjusted social costs, firms respond primarily to recognized financial costs embedded in accounting systems. Therefore, OWCE serves as an accounting-based indicator of the extent to which a firm’s cost structure is financially exposed to water-related charges under prevailing pricing regimes.

The final sample comprises 40 firms observed over 10 years, yielding 400 firm-year observations after a standard data cleaning procedure. The food sector provides an appropriate empirical setting due to its high dependence on water as a critical production input and its exposure to both physical and regulatory water risks.

3.3 Variables definition and interpretations

According to the hypothesis mentioned above, the following variables were used in this research:

3.3.1 Dependent variable

Firm financial performance is measured using return on assets (ROA), a standard indicator of operating profitability and efficiency widely used in corporate finance and sustainability research.

3.3.2 Key explanatory variables

Operational Water Cost Exposure (OWCE) is measured as the ratio of water-related expenses to the total cost of production, capturing the degree to which a firm’s core operations are financially exposed to water-related expenses. This measure reflects the share of production costs directly attributable to water use and compliance rather than the absolute level of water prices or pure market (economic) value.

In contexts where water pricing is administered by the Government, observed water charges provide a relative indicator of a firm’s dependence on water within its cost structure. Higher OWCE implies greater sensitivity of operating margins to water-related costs. Therefore, it serves as a financial exposure signal. However, higher OWCE may not be considered a direct proxy for physical scarcity or even for the managerial strategies employed to manage pressure in their respective contexts. Accordingly, OWCE is an accounting-based indicator of the extent to which a firm’s production cost structure is financially exposed to water-related charges under prevailing pricing regimes. Essentially, it captures the degree to which water-related considerations are embedded in the firm’s operating cost structure and reflects exposure to regulatory scrutiny, compliance intensity, and operational sensitivity within a managed pricing regime.

3.3.3 Control variables

The model includes firm-level controls capturing size, leverage, growth opportunities, and operational characteristics to account for heterogeneity in financial structure and operational characteristics.

Time dummies are included to capture macroeconomic shocks common across firms, such as inflationary pressures and commodity price fluctuations. Table 1 includes the variables applied in this study. Apart from OWCE, operational cost intensity (OCI) is the ratio of cost of goods sold divided by total assets. Then, the net working capital ratio (NWCR) represents a liquidity buffer. Firm size accounts for scale differences across firms. These ratios, expressed in terms of total assets, are interpreted as regressors to add value to the analysis and, therefore, considered appropriate. Firm size is the natural logarithm of average income-generating total assets. It gives a measure of the operational scale from revenue-contributing assets. Furthermore, the ratio of money supply (M3) to gross domestic product (GDP) (1) is used in this study as the financial liquidity index (FLI), a financial development indicator. This is particularly significant as units of the food industry vary greatly in size and are ubiquitous in a country such as India. Since the sample consists of firms operating within a common national regulatory and monetary framework, year fixed effects (FEs) absorb nationwide inflation and commodity shocks. However, including additional macro-level controls would introduce multicollinearity with time dummies.

3.4 Methodological assumptions and limitations

This study employs a dynamic panel framework estimated using the Difference Generalized Method of Moments (D_GMM). The method assumes that unobserved firm-specific heterogeneity is time invariant and can be removed through first-differencing. It further assumes that the lagged values of endogenous variables serve as valid internal instruments, conditional on the absence of second-order serial correlation in the differenced error term. Instrument validity can be assessed using Hansen and Sargan over-identification tests. Serial correlation is examined using AR(1) and AR(2) diagnostics.

The dynamic specification captures inter-temporal adjustment processes by including lagged financial performance. This implies that current firm performance may depend not only on contemporaneous water cost exposure but also on prior-period profitability and adjustment responses. However, the framework primarily captures short- to medium-term financial adaptation and may not fully reflect long-run structural transformations or technological shifts that materialize over extended horizons.

OWCE is constructed from reported accounting expenditures. While this reflects the financial burden recognized in firm statements, it does not capture unpriced environmental externalities or scarcity-adjusted groundwater extraction costs, or even plant-level variations. Many firms operate multi-plant structures across jurisdictions; OWCE represents aggregated firm-level exposure rather than location-specific risk.

Furthermore, time fixed effects may absorb common macroeconomic shocks such as inflation and commodity price volatility. However, the model does not explicitly incorporate regional climatic variability or plant-level water stress indices due to data limitations. Therefore, the estimated relationships should be interpreted as financial adjustment responses to reported water cost exposure rather than direct causal effects of physical water scarcity.

3.5 Empirical approach

To examine how firms adjust financial performance dynamically in response to water cost exposure, this study employs a dynamic panel data model estimated using the difference Generalized Methods of Moments (D-GMM) estimator. The dynamic specification includes lagged firm performance to capture persistence and adjustment processes over time.

The GMM estimator is particularly suitable in this context for three reasons. First, the number of firms exceeds the time dimension, making conventional fixed-effects estimators biased and inconsistent in the presence of a lagged dependent variable. Second, water cost exposure may be endogenous due to simultaneity and omitted variable bias, as more profitable firms may invest differently in water-intensive operations. Third, the estimator effectively addresses unobserved firm heterogeneity and dynamic feedback effects.

Lagged levels of the endogenous variables are used as instruments for first differences following established practice in the dynamic panel literature. Standard diagnostic tests, such as tests for serial correlation and instrument validity, are employed to ensure robustness of the estimates.

Importantly, this empirical strategy does not observe managerial strategies directly. Instead, it captures “revealed financial adjustment behavior” inferred from the observed changes in firm performance over time under varying degrees of water cost exposure. This approach is consistent with prior empirical research examining adaptive firm responses to resource cost pressures using dynamic financial outcomes.

As described above, the Arellano-Bond Estimator, which is also recognized as the difference GMM estimator, is employed, wherein first differences are used in the analysis. In a dynamic panel data model, the dependent variable (DV) is reliant on its own historical values. Stated otherwise, there is a probability of correlation between the specific effect (μi) and the lagged DV, or ROA(i, t − 1). See the Equation 1 below:

As shown in the flow of analysis figure (Figure 1), the analysis begins with descriptive statistics, correlation analysis, and static panel estimations to examine contemporaneous associations and data properties. Diagnostic limitations of static models, particularly persistence in firm performance and potential endogeneity, lead to the use of a dynamic GMM framework, which forms the basis for the final inference.

Figure 1

3.6 Constructing the model

Water related exposure influences the operational performance of firms in multiple ways. We can examine that using panel or time series data analysis. However, performance in terms of asset efficiency is inherently a continuous and dynamic process. Economic theory suggests that current performance is partly dependent on past performance due to adjustment costs, persistence in operations and gradual adaptation mechanisms. Accordingly, the inclusion of lagged dependent variable allows the model to capture these inter-temporal dynamics more accurately. This addition transforms the initial static specification into a dynamic model. Thus, both theoretical expectations and observed firm behaviour underwater related cost pressures are well reflected. The accuracy of the conclusion can only be ensured by employing the dynamic panel data model. In order to evaluate the impact of water more accurately, by incorporating the ROA lag feature, a dynamic panel data model based on generalized moment estimation was chosen. Through the creation of dynamic panel data models, the purpose of this work is to directly examine the relationship between ROA and OWCE.

Control factors that impact the firm’s profitability are also introduced to the model, along with the first lag of ROA, taking into account the fact that ROA is influenced by factors not only in the present period but also in the past. Therefore, the following dynamic panel model is proposed through Equation 2 below:

In this case, X₁kit or the first independent variable (IV) is the primary regressor as the “ratio of water cost to cost of production”(OWCE). Additionally, X₂kit or the second IV is the “Operational Cost Intensity” (OCI), X₃kit or the third IV is the “ratio of net working capital to total Assets”(NWCR), X₄kit or the fourth IV is log of size (l_Size), and X₅kit or the fifth IV is “Financial Liquidity Index” (FLI), which acts as the control variable. Also, β₁i, β₂i, β₃i, β₄i, and β₅i are the coefficients of IVs, respectively, μi denotes individual effects, and εit is a random error term. ROAit is the DV, and ROA_it − 1, ROA_it − 2 are the lagged DV for use in the analysis.

The individual effect and the lagged value of the DV are expected to be correlated, which will lead to endogenous issues. Since the least squares approach alone is not suitable for regression in this model, the generalized method of moments (GMM) may be better suited to this study. There exist both short- and long-run relationships between IVs and DV, and the water variable has some impact on the performance of the firm. However, GMM provides short-run results, and it may be inconsistent for the long run. The dynamic panel specification includes lagged profitability to capture persistence and adjustment dynamics. The coefficient on OWCE reflects the short-term association between current water cost exposure and profitability within the same accounting period. This approach captures financial stress responses rather than long-term capital restructuring. Longer-term adaptation processes, such as infrastructure investments or supply chain restructuring, may occur over multi-year horizons and may not be fully observable within annual dynamic specification. Based on the analysis steps, stationarity tests are done and summarized in Table 2. Final inference is based on dynamic panel estimates.

Panel unit root tests confirm stationarity of variables, supporting the use of dynamic panel estimators. Differencing employed is limited to first- and second-order lags, and does not go beyond.

4 Results and discussion

Firms in water-intensive sectors commonly adopt adaptation strategies, such as wastewater recycling systems, membrane filtration technologies, zero-liquid discharge systems, process redesign, and supply chain adjustments. Implementation costs vary widely depending on plant location, size, and regulatory requirements, which may involve significant upfront capital expenditure with multi-year amortization. As such, adaptation responses may not fully manifest within a single accounting period. The empirical model captures short-term profitability responses, which may precede observable capital reconfiguration. The time period selected ranges between 2014 and 2024 to fairly capture the changes and effects of most of the adaptations by firms in light of the intensified water sustainability discussion. For each firm in the sample, a survey of water initiatives was conducted from their publicly disclosed information (websites and reports). It was found that a majority of firms have implemented plant-level effluent treatment and water reuse systems. A smaller subset has adopted capital-intensive technologies, such as advanced process optimization, tertiary recycling, and zero-liquid discharge. Rainwater harvesting and groundwater recharge measures are also widely reported, particularly in locations with higher water risk. Community- and CSR-oriented water projects, such as pond rejuvenation, irrigation support to farmers, RO plants, and drinking water access facilities, are common, though their linkage to operational water efficiency varies. These observations reflect that many adaptation responses affect operating costs and profitability within the annual accounting period. The more transformative investments may unfold over longer horizons, aligning with the dynamic modeling approach.

To achieve the research objectives, the analysis was performed as follows. Descriptive statistics of the major variables are presented in Table 3, the correlation matrix in Table 4, the variance inflation factor in Table 5, and the generalized method of moments estimation results in Table 6.

As shown in Table 3, variability across the sample can be observed in standard deviation values. Some variables, such as the FLI, exhibit low dispersion around the mean (0.03), whereas others, including OWCE and return on assets (ROA), show greater variability. This indicates that firm-level data fluctuate substantially over time.

Table 4 shows the negative correlation between operational water cost exposure (OWCE) and the profitability ratio or ROA. In this context, the relationship reflects contemporaneous accounting exposure rather than causal inefficiency. Given OWCE’s construction as a cost–structure ratio, such an association is expected in static analyses, motivating the use of dynamic models to capture adjustment effects over time. Firm size in this table demonstrates its relationships with ROA and NWCR, supporting the use of average income-generating assets to ensure consistency between annual performance flows and asset utilization. Furthermore, ROA is negatively correlated with the Financial Liquidity Index (FLI) and the Operational Cost Intensity (OCI). Correlations among the remaining explanatory variables remain below the accepted threshold of 0.7, indicating no serious multicollinearity concerns.

All explanatory variables display variance inflation factors (VIFs) well below the threshold value, confirming the absence of multicollinearity, as shown in Table 5.

Unit root tests for all variables are conducted in Eviews, ensuring stationarity of the data. This process also helped in selecting variables for the study.

Table 6 reports the estimation results from both static panel models and the dynamic GMM specification. In the static models, water cost exposure exhibits a negative association with ROA. This finding is consistent with conventional views that higher resource costs reduce firm profitability in the short run. This finding aligns with prior studies emphasizing the cost burden of water use, particularly in water-intensive industries (Zheng et al., 2022). However, static specifications capture only concurrent accounting effects and do not allow for intertemporal adjustments in firm performance. Using the GMM estimator, dynamic adjustment is incorporated, revealing that the relationship between water cost exposure and firm performance remains statistically significant. The coefficient of the lagged dependent variable becomes positive and significant (at the second lag), confirming persistence in firm profitability and validating the dynamic specification. However, the small coefficient on lagged ROA reflects limited persistence in profitability over the period. Standard diagnostic tests indicate no second-order serial correlation and confirm the validity of the instrument set. While AR(1) is not statistically significant, this does not invalidate the model, considering the short time dimension and the use of robust standard errors. Moreover, the absence of AR (2) supports model consistency.

4.1 Validity tests

The contrast between static and dynamic estimates provides critical insight into how firms respond to water cost exposures. While static models capture the immediate accounting effect of water-related expenses, the dynamic results suggest that firms exposed to higher water cost pressures undertake adaptive adjustments that improve financial performance over time. The sign reversal thus does not imply that higher water costs are inherently positive; instead, it indicates that sustained exposure induces adaptive financial and operational responses that offset initial cost pressure over time.

This finding is consistent with the literature highlighting the role of operational efficiency and technological adoption in mediating the financial impacts of water management initiatives. Firms appear to respond to sustained water cost exposure by reallocating resources, improving process efficiency, and optimizing scale, thereby offsetting initial cost pressures.

Importantly, in contexts where water pricing is subsidized, observed water charges may also reflect broader regulatory visibility and legitimacy concerns rather than only a market price perspective. As such, water cost exposure functions as a signal of underlying risk, incentivizing proactive adjustments rather than passive cost absorption.

Sub-sample analysis is performed based on the median between high-exposure and low-exposure firms. As per Table 7, the results from the sub-sample analysis meaningfully reveal heterogeneity in the profitability effects of OWCE. Among high-exposure firms, OWCE exhibits a strong and statistically significant negative association with ROA. Along with negative profit persistence, this suggests heightened vulnerability and adjustment stress. In contrast, the OWCE coefficient for low exposure firms is imprecisely estimated, while profitability displays strong persistence, which can be interpreted as greater operational buffering capacity. However, instrument validity is weaker in the high-exposure sub-sample and calls for more examination with additional data.

Nonetheless, this heterogeneity aligns with prior findings that financial benefits from water management and sustainability initiatives are contingent on firm capabilities and exposure levels (Brears, 2023; Barbosa and Pumpín, 2024). Firms with limited exposure may lack sufficient incentives to undertake costly adjustments, whereas highly exposed firms are more likely to pursue efficiency-enhancing and risk-mitigating responses.

The results contribute to the growing literature that reframes water costs as a component of financial exposure rather than a simple input price. They suggest that water-related pressures can catalyze adaptive behaviors and, in turn, strengthen firm performance over time. For firms stances on water please refer to Supplementary File in Appendix 1. These findings complement studies emphasizing financial instruments, cooperative arrangements, and operational efficiency as mechanisms through which firms manage water-related risks (Larson et al., 2012; Liu et al., 2024; Hamilton et al., 2024). Rather than contradicting the cost burden perspective, the results highlight the importance of temporal dynamics in understanding how firms respond to resource constraints.

5 Conclusion and recommendations

This study examined how food and beverage firms adjust financial performance dynamically in response to water cost exposure. Moving beyond static interpretations of water costs as a simple input price, the analysis conceptualizes water-related expenses as indicators of broader operational and financial exposure. Using a dynamic panel framework, the study captures firms’ revealed adjustment behavior under sustained water cost pressures. The study shares a related empirical setting done earlier by the authors, thought it differs in conceptual framing and analytical focus¹.

These findings reveal that water cost exposure is negatively associated with lower profitability in static models. However, once dynamic adjustment is accounted for, this relationship reverses and becomes positive and statistically significant. Specifically, the dynamic effect suggests that firms respond to persistent water-related pressures through adaptive mechanisms, such as efficiency improvements, process optimization, and scale adjustments. Such adjustments outweigh the initial cost burdens over time. This result helps reconcile mixed evidence in prior literature by demonstrating that short-run cost effects and longer-run adaptive responses can coexist. These findings are consistent with Transaction cost economics, which predicts efficiency seeking responses under cost uncertainty and institutional theory. They highlight the role of regulatory and legitimacy pressures in shaping firm adaptation to water-related risks.

More importantly, the results indicate that water costs function not only as explicit monetary charges but also as signals of broader financial exposure, such as regulatory scrutiny, reputational considerations, and operational continuity risks. Even when direct prices are low, these associated pressures may incentivize firms to internalize water-related cost pressures embedded in the production structure and to undertake performance-enhancing adjustments. The study thus contributes to a growing body of research that reframes water as a material financial risk rather than solely an environmental or operational concern. Unlike cost-intensity approaches, the OWCE measure captures multi-dimensional financial exposure, including regulatory and operational pressures. This portrayal enables analysis of dynamic adjustment mechanisms.

From a managerial perspective, the findings highlight the importance of proactive adaptation to water-related pressures rather than viewing them purely as compliance costs. Investments in operational efficiency and adaptive capabilities appear central to converting water cost exposure into improved financial outcomes. Beyond firm-level performance implications, the study contributes to sustainability accounting discourse by illustrating how operational resource cost exposure can be integrated into empirical financial performance analysis. For policymakers, the results suggest that water pricing and regulatory signals can influence firm behavior beyond their immediate cost impacts, reinforcing the role of policy design in shaping long-term corporate responses. For investors, the study underscores the relevance of water-related exposure in assessing firm resilience and financial performance.

Despite these contributions, the study is subject to several limitations. First, water cost exposure is measured using reported accounting expenditures, which do not capture unpriced environmental externalities, scarcity-adjusted social costs, or plant-level heterogeneity. As many firms operate across multiple locations, OWCE reflects aggregated firm-level exposure rather than jurisdiction-specific water risk. More granular plant-level data, tariff disaggregation, and physical water stress indices could improve measurement precision.

Second, firm disclosures do not clearly distinguish between utility-based procurement and groundwater extraction costs in sufficient detail. This limits the ability to separate regulatory from physical scarcity exposure. Third, although time fixed effects absorb macroeconomic shocks, the dataset does not explicitly incorporate region-specific climatic variability or supply chain disruptions due to extreme climatic conditions, such as droughts. Access to higher frequency operational or climatic data could enhance causal interpretation.

Future research may develop more refined composite indices of water-related exposure by incorporating physical water stress indicators, tariff structures, groundwater dependency, and regulatory stringency. Extending the analysis to other water-intensive sectors, such as textiles, chemicals, power generation, electronics, and mining, could help assess sectoral heterogeneity in dynamic adaptation patterns. Cross-country comparative studies across pricing regimes would further clarify how institutional contexts shape financial adjustment processes. Additionally, integrating plant-level or supply chain-level data could improve the understanding of localized adaptation responses.

Overall, this study represents a step toward contextualizing water-related exposure for firms using a dynamic model. It provides empirical evidence that operational resource cost exposure can be integrated into dynamic financial performance analysis, thereby advancing sustainability accounting and corporate water risk research.

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