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# How Water Flows Through CGE Models: A Systematic Review

Martin Henseler, Helene Maisonnave, Enzo Salvatore

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Martin Henseler, Helene Maisonnave, Enzo Salvatore. How Water Flows Through CGE Models: A Systematic Review. 2025. hal-05263526

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# How Water Flows Through CGE Models: A Systematic Review

Martin Henseler<sup>a,*</sup>, Helene Maisonnave<sup>b</sup>, Enzo Salvatore<sup>a</sup>

<sup>a</sup> Université Rouen Normandie, LERN UR 4702, Rouen, France

<sup>b</sup> Université Le Havre Normandie, EDEHN UR 7263, Le Havre, France

**Abstract**

Water scarcity, climate change, and pollution increasingly threaten ecosystems and economies, requiring tools that capture their complex interactions. Computable General Equilibrium (CGE) models are widely applied for this purpose, linking water resources with production, households, and trade. Despite rapid growth since 1995, research remains fragmented and methodologically diverse. Our review of studies from 1995–2025 shows strong expansion after 2010, with emphasis on climate change, scarcity, and resource management, but limited focus on floods, pollution, and household impacts. Regionally, Asia—especially China—dominates, while Africa and hydrological regions such as river basins are underrepresented. Representation of water as a production factor shows both flexibility and variation across studies. Advancing CGE-water research will require greater methodological standardisation, stronger integration with hydrological models, and broader regional coverage.

**JEL-Code:** C68, Q25

**Keywords:** water, WEFE-nexus, economic assessment, simulation, literature review

**Acknowledgments**

This research was supported by the European Union's Horizon Europe research and innovation program, project InnWater (Promoting Social Innovation to Renew Multi-level and Cross-sector Water Governance, Grant No. 101086512), and by Région Normandie, project MoWat (Modélisation de la gestion intégrée des usages de l'eau – une approche micro-macro).
# How Water Flows Through CGE Models: A Systematic Review

## 1 Introduction

Water is a vital resource that underpins ecosystems, sustains economies, and is essential for food, industry, and agriculture (Dinar 2014; Hoekstra, 2011; Meybeck, 2004; Oki & Kanae, 2006; Postel et al., 1996). However, the intensifying impacts of climate change have exacerbated water scarcity and drought, not only in traditionally arid regions but increasingly in areas previously considered water-rich. Seasonal droughts place growing pressure on water users across sectors (Ragab & Prudhomme, 2002; Ludwig et al., 2014; Gunda et al., 2025; Richardson et al., 2023). Concurrently, water pollution driven by industrial and agricultural activity diminishes the availability of clean water, while extreme weather events, such as floods, further compromise infrastructure and economic assets (Mishra, 2023; Zahoor & Mushtaq, 2023; Gopalakrishnan, 2013; Willner, et al., 2018). The economic damages resulting by floods and water pollution of the past 40 years account in Europe for around 400 billion Euros (European Commission, 2025). These realities highlight the urgent need for robust frameworks to understand the interdependencies between water, economies, and policy, and to ensure sustainable and equitable access to this vital resource (European Commission, 2025). Thus, improving water governance is an urgency on the agendas of regional and of international policy institutions, such as the European Water Framework Directive, the Water Resilience Strategy (European Commission, 2025) and the United Nations Sustainable Development Goals on clean water and sanitation (SDG 6) and life below water (SDG 14) (United Nations, 2025).

Computable General Equilibrium (CGE) models have become widely used tools for analysing the environmental and economic impacts of water-related issues and assessing the effectiveness of water policies. Applications range from allocation rules (see e.g. Blignaut & Van Heerden, 2009; Diao et al., 2008; Fang et al., 2016) to pricing and tariff reforms (Berrittella et al., 2008; Letsoalo et al., 2007; Tian et al., 2021), and restrictions on water use (Briand et al., 2023; Berrittella et al., 2007; Rose & Liao, 2005). These studies demonstrate how CGE models capture the economy-wide consequences of water scarcity and policy design, linking sectoral shocks to broader welfare and distributional outcomes (see also Koopman et al., 2017; Wang et al., 2015; Shahpari et al., 2022; Qin et al., 2012; Zhong et al., 2015).

Unlike partial equilibrium or engineering-based approaches, CGE models capture feedback loops across markets and agents, making them especially valuable for assessing policies on allocation, pricing, and climate adaptation. Water-focused CGE models, in particular, allow researchers to assess how water availability and policy interventions affect economic outcomes, and conversely, how economic activity influences water use and quality. Thus, water CGE model represent the interactions between economy, society and the environmental resource water. As a result, over the past 25 years, the use of CGE models in water research has grown rapidly, reflecting both advances in data and modelling capacity and an increased demand for systemic assessments (Dinar, 2024; Stroombergen, 2017; Wittwer, 2012; Berrittella et al., 2007; Calzadilla et al., 2011; Dolan et al., 2021). At the same time, the literature on CGE-water modelling remains fragmented. Reviews have highlighted key applications—such as irrigation policies, tariff reforms, and drought management—but often treat water as a secondary topic within broader energy or climate assessments. Recent analysis of frameworks like the Water Energy Food Environment (WEFE) nexus approach or policy strategies, like the recently released European Water Resilience Strategy (European Commission, 2025) emphasise the need forholistic approaches, considering intersectoral impacts, for which CGE models are suitable analysis tools. Yet, no comprehensive,
systematic review has traced the evolution of CGE-water studies, their methodological assumptions, and their regional and thematic biases.

This article addresses this gap. We systematically review 157 studies published between 1995 and 2025 that explicitly integrate water into CGE models. Our objectives are fourfold: (i) to map the evolution of this field and its disciplinary anchoring; (ii) to identify dominant themes and underexplored issues; (iii) to compare methodological choices, particularly the treatment of water as a production factor; and (iv) to highlight implications for future research and policy. In doing so, we aim to provide a resource for researchers and decision-makers seeking to strengthen the role of CGE modelling in sustainable and equitable water governance.

Although several reviews have addressed water-focused CGE modelling, most of them appear either as brief sections within broader studies or as stand-alone literature reviews that offer high-level summaries of research scopes, model structures, and data requirements. These existing reviews typically describe the primary applications of water CGE models, highlight their policy relevance, and outline the typical treatment of water as a production factor or environmental constraint. For instance, Dinar (2014), Dudu & Chumi (2008) and Johansson (2005) present the development of water- integrated CGE models from 1995 to the early 2010s, while noting the lack of methodological standardisation, a unified classification framework, and integration between micro- and macro- level analyses.<fnref n="1" /> Authors like Bekchanov et al. (2017), Calzadilla et al. (2016), Hertel & Liu (2019), Griffith (2012) and Ponce & Giupponi (2012) go further by unpacking water modeling techniques (intermediate inputs, CES structures, parameter calibration), and emphasising the need for fine sectoral disaggregation, water-input substitution options (water composite, land-water composite, capital-water composite, irrigated water composite, model structure, etc.), and explicit treatment of spatial, seasonal, and sectoral heterogeneity. While several recent reviews (Bardazzi & Bosello, 2021; Raviv et al., 2022; Castelli et al., 2024; Dinar, 2024) situate water within the broader WEFE nexus and macroeconomic frameworks—including CGE models—they all highlight persistent methodological fragmentation, a lack of realism in water-resource modeling, and the absence of a single, coherent framework for its integration.

Although CGE-water studies proliferated and tackle ever more interdisciplinary topics (Figure 2), their growth outpaces the ability of review to analyse them and capture the most recent issues. Existing review are mainly descriptive (Brouwer & Hofkes, 2008; Griffith, 2012; Johansson, 2005; Ponce & Giupponi, 2012) or required systematic updates (Dudu & Chumi, 2008; Dinar, 2014). Calzadilla et al. (2016), Fadali et al. (2012) and Hertel & Liu (2019) do broaden the scope, but without a systematic protocol for gathering studies. Despite recent syntheses devoted to the WEFE nexus in macroeconomic models (Castelli et al., 2024; Dinar, 2024; Li et al., 2020; Raviv et al., 2022), only Bardazzi & Bosello (2021) and Bekchanov et al. (2017) apply a systematic, reproducible approach that enables a quantitative analysis of trends in the CGE-water literature.

This article advances the literature on water-focused Computable General Equilibrium (CGE) models in four main ways. First, it provides the most comprehensive and up-to-date systematic review of the field to date. By analysing 157 studies published between 1995 and 2025, this study substantially extends the temporal and thematic coverage of previous reviews, most of which were either limited in scope or dated.

<footnote n="1">¹ We present a comparison of selected literature reviews and our study in the Appendix in Table A-1-1 and in an extensive form in Table A-1-2.</footnote>
Second, it introduces a novel methodological classification of how water is represented in CGE production structures. We systematically identify and compare 22 distinct types of “production trees,” highlighting differences in the treatment of water as a factor of production, its nesting with other inputs, and the assumptions about substitutability. This classification exposes both the flexibility and the methodological inconsistencies of existing models.

Third, the review systematically documents regional, thematic, and disciplinary biases. It shows the dominance of studies focused on Asia—particularly China—while identifying major research gaps in Africa and at the scale of natural hydrological regions. Thematic gaps are also highlighted, notably the underrepresentation of household-level impacts, floods, and pollution.

Finally, the article situates CGE-water modelling within broader interdisciplinary and policy debates, including the WEFE nexus and recent resilience strategies. In doing so, it emphasises the potential of CGE models to inform integrated and equitable water governance.

## 2 Methodology

For the systematic literature review, we followed the PRISMA 2020 guidelines (Page et al., 2021). We constructed comprehensive database of retrieved and analysed articles. We conducted the initial search by using keyword queries in the Web of Science database (for detailed search operators, see Annex 7.2.1). This process yielded 223 records of indexed academic articles published between 1995 and February 2025. We downloaded the accessible full-text articles. In further analysis we evaluated the full texts by using a standardised content analysis sheet. We assessed the articles according to a set of predefined categories and attributes (for detailed presentation of the content analysis sheet see, see Annex **错! Source du renvoi introuvable.**).

## 3 Results

### 3.1 Identification and screening

Figure 1 schematically presents the process of article identification and selection. Using a keyword search targeting CGE models and water-related topics, we retrieved 223 potentially relevant records from the Web of Science database. As Web of Science provides broad coverage of peer-reviewed academic literature and includes various indexed repositories, we consider this sample—with its relatively high number of articles—to be representative of past and current publication trends in this research area. We successfully downloaded the full texts for 213 of these articles and identified 157 as relevant for this study. From this subset, 37 articles were selected for detailed analysis of production structures (i.e., production trees).
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Number of articles, identified selected and included, adapted from Page et al. (2021)</figcaption></figure>

Note: <sup>a</sup> Not relevant because: water not modelled explicitly modelled as a production factor (n = 31), paper is not a CGE or a water related study (n = 6), paper does not present a case study (n = 16)

## 3.2 Research disciplines

In their pioneering work, Berck et al. (1991)<fnref n="2" /> introduced CGE models as a framework to assess the impacts of water policies, taking into account intersectoral linkages. Early contributions to the field of water-related CGE modelling were primarily published as book chapters, working papers, or technical reports.<fnref n="3" /> The first journal articles in this area appeared later, including works by Seung et al. (1997, 1998), Foulo & Grafton (1998) and Chisari et al. (1999). Since these early publications, the number of research articles using CGE models to analyse water-related issues has grown substantially. After 2010, this growth exhibits an exponential trend. Part of this increase reflects the general rise in academic publications across disciplines and methodologies. Additionally, advances in computational performance and greater accessibility to standard CGE models have facilitated their development and application (Bolarinwa, 2024). The growing availability of data (e.g., the availability of Social

<footnote n="2">² Note: According to Ponce & Giupponi (2012), the authors Lofting & McGauhey (1968) were the first to introduce water into CGE models, in their study *Economic Valuation of Water. An Input-Output Analysis of California Water Requirements. Contribution No. 116. Water Resources Center*.</footnote>

<footnote n="3">³ Berck et al. (1991) was published as a book chapter. We focus our analysis on journal articles, thus, this early work did not enter our sample which starts just in 1998, first article by Seung et al. (1997 & 1998).</footnote>
Accounting Matrixes) has further supported the expansion of CGE-based research and publications (United Nations, 2014; Babatunde et al., 2017). For instance, institutions such as International Food Policy Research Institute (IFPRI) or the European Commission's Joint Research Centre (JRC) have worked on providing publicly available SAM at the country level, and even at the regional level (García-Rodríguez et al., 2025).

The number of CGE studies focusing specifically on water has increased, reflecting the growing importance of water in economic systems. More frequent occurrences of droughts, water scarcity, floods, and water pollution worldwide have highlighted the urgent need for improved water governance and resource management (Antunes et al., 2009; Kompas et al., 2024). In response, global, national, and regional institutions have implemented a range of water-related policies, such as the European Water Framework Directive and similar initiatives, such as the European Water Framework Directive. **Erreur ! Source du renvoi introuvable.** illustrates the evolution of water-related CGE publications across various research disciplines over time. As a vital natural resource—serving both as a production factor across many economic sectors and as a key component of ecological systems— water requires integrated approaches. CGE models have proven to be a suitable tool for analysing water-economy interactions (Bekchanov et al., 2017; Cai, 2008).

Initially emerging in the 1990s as an economic analysis tool, water focused CGE models began expanding into other disciplines after 2010, including environmental science, engineering, and earth and planetary sciences. Over the years, most water CGE studies have been published in economics journals (36.9%) (see
Table 1). Due to their focus on water as a natural resource, a significant portion has also appeared in environmental science (24.8%) and earth and planetary science journals (19.7%). Publications in engineering journals account for 12.7%, making them the fourth largest category, while the remaining articles are distributed across interdisciplinary, physical, and other scientific fields. The share of publications in earth and planetary science journals has remained relatively stable, with exceptions in 2021 and 2023. Meanwhile, the share of articles in economics and environmental science journals has shown a slight upward trend. This suggests that, over the years, water related CGE research has consistently maintained its relevance primarily within the fields of economics and environmental science.

<figure><img src="image_4.png" /><figcaption>Figure 2: Evolution of research articles over time in research disciplines.</figcaption></figure>

## 3.3 Research topics global

Across different research fields, the focus of CGE-based water studies varies significantly. The majority of articles (over 26.8%) concentrate on water resource management and water price (14%) (see Table 1). Other prominent research areas include energy (15.9%), environmental issues (7.01%), and agriculture and food production (11.5%). The number of articles addressing agri-food topics has increased notably since 2010, while research focusing on energy has risen sharply since 2015 (see **错误! 源头 du renvoi introuvable.**). These developments coincide with global events such as food and energy price spikes and the growing emphasis on climate protection and related policies. Interestingly, despite the suitability of CGE models for analysing price and tax policies (Boudmyxay et al., 2019; Luckmann et al., 2016a; Wang et al., 2016), only a few articles since 2015 have focused on water pricing. This is noteworthy given the increasing importance of water scarcity and resource management. The limited number of studies on water pricing may indicate a research gap, possibly due to methodological challenges in applying CGE models to water pricing analysis (Dudu & Chumi, 2008).

In recent years, CGE studies have also started addressing questions related to the Water-Energy-Food-Ecosystems (WEFE) nexus. These models allow for an economy-wide perspective and the analysis of intersectoral impacts, with a focus on the four nexus pillars:
Water, Energy, Food, and Ecosystems (Liu, et al., 2016; Nechifor & Winning, 2017; Taheripour, et al., 2013; Teotónio, et al., 2020; Sun, et al., 2021). Articles focusing solely on water—as the first pillar of the WEFE nexus—make up the second-largest category (21.6%) among the analysed studies (see
Table 1). However, the largest share (36.6%) addresses cross-cutting topics between water and the environment, particularly related to climate change. Fewer studies examine the nexus linkages between water and energy (11.8%) or water and food (8.5%). The proportion of studies covering two or more WEFE pillars remains small, suggesting that the full potential of CGE models in this area has yet to be fully explored.

To conduct impact assessments, CGE models simulate changes in economic or environmental systems, typically in the form of scenario-based shocks. These scenarios vary in assumptions and scope. The most common scenarios model climate change impacts (39.4%), followed by water scarcity and shortages (31%), and drought events (16.9%) (see
<figure><img src="image_5.png" /><figcaption>Figure 3: Evolution of research articles over time and the research topics</figcaption></figure>

## 3.4 Research topics in world regions and countries

CGE models are typically developed and applied to specific regions or countries, where they simulate corresponding research topics. Table 1 presents the distribution of CGE-water studies among world regions. More than the half of the studies focus on Asia, followed by Europe and Africa, with each 12 to 13% and other regions with less than 10%. Figure 4 presents the distribution of research topics varies among world regions. Agriculture and climate change are addressed relatively evenly across all world regions, reflecting their global significance and widespread relevance (Sartori et al., 2024; Faust et al., 2015; Mushtaq et al., 2015; Sánchez Chóliz & Sarasa, 2019; Vargas et al., 2018). In contrast, research on water scarcity is most prominent in studies focused on Asia and Africa, where the issue is projected to become increasingly critical (Panta et al., 2023; Shahpari et al., 2022; Juana et al., 2012). For most other research topics, Asia accounts for the largest share of studies, likely driven by high research capacity and better data availability (Jin et al., 2019; Miftah et al., 2023; Knowling, et al., 2020). Meanwhile, Europe, the Americas, and Africa show similar levels of research activity, suggesting a balanced interest in these areas. Water pollution, although addressed in only a small number of studies, appears to be a concern mainly in Asia and Africa—regions where water-related issues are especially pressing (Fang et al., 2016; Kyei & Hassan, 2019; Osman et al., 2019; Guo et al., 2018).
Distribution of 9 classes by world region

<figure><img src="image_6.png" /><figcaption>Figure 4: Distribution of research topics among world regions</figcaption></figure>

Similar to world regions, which display distinct research priorities, the country-level distribution of topics also reveals a strong concentration. Figure 5 presents the occurrence of research topics in countries with at least five observations. China dominates research output across nearly all categories, with the exception of water scarcity and agriculture. China is particularly prominent in studies related to carbon emissions and trade (Chen et al., 2010; Fan et al., 2018; Yin et al., 2024; Zhao et al., 2021), reflecting China's dual role as the world's largest emitter of greenhouse gases and a central player in global trade networks.

South Africa features prominently in research on climate change and water scarcity, which aligns with expectations given the country's high vulnerability to both issues (Juana et al., 2012, Briand et al., 2023; Blignaut & Van Heerden, 2009). However, only a few other Sub-Saharan African countries are represented (Shahpari et al., 2022; Schuenemann & Hess, 2023), highlighting a considerable research gap in a region that is among the most affected by climate change and water scarcity (Kusangaya et al., 2014). Among European countries, Spain ranks second in terms of representation in these same research areas, reflecting its exposure to significant water stress (Cazcarro et al., 2020; Philip et al., 2014; Cazcarro et al., 2011).

Australia, as both a country and a continent, appears with a balanced distribution across five categories. However, it is notably underrepresented in studies focusing on energy, water trade, and climate change—an unexpected finding given its environmental context. This result can be largely explained by the fact that Australian research has chiefly examined agricultural impacts, most notably drought in the Murray-Darling basin (Wittwer & Griffith, 2011), the relocation of certain crops due to climate change (Mushtaq et al., 2015), and pollution issues affecting the Great Barrier Reef (Smajgl & Hajkowicz, 2005; Smajgl et al., 2009).
<figure><img src="image_7.png" /><figcaption>Figure 5: Distribution of research topics among countries with at least 5 observations.</figcaption></figure>

3.5 Regional resolution

CGE models are applied at varying spatial resolutions. The most common are national-level
models (i.e., single-country analyses), which account for 33.8% of the sample. These are
followed closely by single-region studies (33.1%), which may include either sub-national
regions or regional groupings across several countries. Multi-region analyses are less
common, making up 22.1% of the studies (Table 1). Since 2015, the number of both multi-
country and single-country studies has increased, reflecting a sustained focus on national-
level analysis (see **错! Source du renvoi introuvable.**) This trend is largely driven by
better data availability at national and multinational levels. Because water scarcity is
inherently a regional issue, there is growing interest in CGE studies conducted at the sub-
national or regional level. However, compiling the necessary data—such as regional SAMs—
poses a significant challenge for regional water CGE modelling (Jia et al., 2017; Fadali et al.,
2012; Raviv et al., 2022).
Evolution of research articles over time and study regions

<figure><img src="image_8.png" /><figcaption>**Figure 6 : Evolution of research articles over time and study regions**</figcaption></figure>

Another dimension of regional characterisation in CGE models is the type of region used in the analysis. The majority of studies (68.8%) are based on administrative regions, such as countries or political subdivisions. Only a few studies represent natural regions, such as river basins (see
Table 1). Natural regions are particularly valuable for water impact assessments, as they better capture physical water availability and hydrological boundaries. However, data for calibrating CGE models in these regions is rarely available and often requires costly and time-consuming research efforts (Jia et al., 2017; Fadali et al., 2012).

Figure 7 presents the distribution of natural and administrative regions per research topic. The results indicate that CGE models using natural regions are predominantly applied to research on water pollution and water management (Smajgl & Hajkowicz, 2005; Smajgl et al., 2009; Ferrarini et al., 2020; Deng et al., 2011; Qin et al., 2013). These findings underline that natural boundaries are especially relevant for studies focused on environmental and resource management.

In contrast, CGE models based on administrative regions are mainly used for analysing water scarcity, water pricing, energy-related carbon emissions, and climate change (Palatnik et al., 2025; Berrittella et al., 2007; Ma et al., 2022; Tian et al., 2022; Vöhringer et al., 2019). These topics typically address impacts at the national level or involve economic mechanisms, such as water pricing, that are implemented at that scale.

It is somewhat unexpected that water scarcity is not more frequently studied using natural regions, given that limited water resources are more closely tied to natural units like river basins and water bodies rather than administrative borders. However, this may be due to the greater availability of water quantity data at the administrative level, which facilitates its integration into CGE models. Moreover, studies that have examined this issue at this scale have sometimes looked at it across several countries (Berrittella et al., 2007; Palatnik et al., 2025), while other have approached it at the national level (Blignaut & Van Heerden, 2009; Shahpari et al., 2022; Juana et al., 2012). The assessment of water scarcity is first used to evaluate the potential impacts for each country and is then complemented by hydrological modelling aligned with the corresponding economic scale.

<figure><img src="image_9.png" /><figcaption>Figure 7: Distribution of research topics among administrative and natural regional resolution.</figcaption></figure>
3.6 Temporal resolution

CGE models are also used at different temporal resolutions. Comparative static models simulate a transition from one point in time to another without accounting for the dynamic processes in between. Recursive dynamic CGE models, in contrast, simulate developments over a defined time period, capturing year-to-year changes. The use of dynamic models has become increasingly common in water-focused CGE research. However, some studies use extremely long simulation horizons (e.g., over 50 years). Such long-term simulations may introduce significant uncertainties and methodological challenges, such as considering changes in agents economic behaviour (Fujimori et al., 2017; Mushtaq, 2016; Zhou, et al., 2018).

Between 1998 and 2015, publications largely favoured static models over dynamic models. Static models are more commonly used to explore issues related to water management and water pricing (Lin, et al., 2021; Wu et al., 2014; Rivers & Groves, 2013; Llop & Ponce-Alifonso, 2012). This may be due to the fact that pricing mechanisms—such as taxes and tariffs—represent immediately implemented policy shocks, much like most tools used in water management (e.g., allocation policies). As a result, researchers tend to focus on short-term, immediate impacts when addressing these topics. Furthermore, in static CGE models, the accumulation of factors is not represented. In dynamic CGE models the accumulation of water may be challenging, requiring exogenous information and assumptions (e.g., based on hydrological models and data).

In contrast, due to the emergence and diversification of research themes, dynamic models are more frequently applied to research questions concerning energy, carbon emissions, climate change, and agriculture (Bachner et al., 2014; Schlör & Schubert, 2022; Ferrarini et al., 2020). These areas are generally studied with a long-term perspective and often involve processes that evolve over time, particularly in the case of climate change. Agricultural research is frequently connected to climate-influenced water availability, which introduces a dynamic element similar to that seen in climate change studies.
<figure><img src="image_10.png" /><figcaption>Figure 8: Evolution of research articles with static or dynamic temporal resolution</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 9: Distribution of research articles with static or dynamic temporal resolution</figcaption></figure>

Figure 10 investigates the relationship between research questions and the simulation time horizons employed to address them. In Figure 10 various categories of research topics are mapped against their respective simulation periods. In this analysis, short-term horizons are defined as those spanning 1 to 3 years. This category also includes comparative static models, which are typically designed to capture short-term shocks. Such short-term simulations are predominantly applied to research questions concerning water pricing and management, water scarcity, and pollution—issues generally characterized as preliminary or short-duration
phenomena. Within these topic areas, models with shorter simulation horizons are especially prevalent (Li et al., 2019; Wang et al., 2015; Zhong et al., 2016; Osman, et al., 2019).

In contrast, articles addressing long-term (3 to 9 years) and very long-term (>30 years) horizons are more commonly associated with climate change, as well as carbon and energy topics (Guo et al., 2020; Su et al., 2023; Fujimori, et al., 2017). These research areas inherently involve processes and impacts that unfold over extended periods, thus necessitating models with longer time frames. Agricultural research questions, however, exhibit a more balanced distribution across short- and long-term horizons. This reflects the cross-cutting nature of agriculture, which is influenced both by immediate events such as water scarcity and by long-term climatic changes (Mushtaq et al., 2015; Lennox & Diukanova, 2011; Kompas et al., 2024; Farajzadeh et al., 2024).

<figure><img src="image_12.png" /><figcaption>**Figure 10: Distribution of research articles with static or dynamic temporal resolution**</figcaption></figure>
<table><caption>Table 1: Numbers of studies per attribute in percent of total</caption><thead><tr><th></th><th>Total</th><th></th><th>Total</th></tr></thead><tbody><tr><td><strong>Research disciplines</strong></td><td></td><td><strong>Study world regions</strong></td><td></td></tr><tr><td>Economics</td><td>36.9</td><td>Asia</td><td>52.1</td></tr><tr><td>Environmental science</td><td>24.8</td><td>Europe</td><td>12.7</td></tr><tr><td>Earth and Planetary Science</td><td>19.7</td><td>Africa</td><td>12</td></tr><tr><td>Engineering Science</td><td>12.7</td><td>World</td><td>7.75</td></tr><tr><td>Interdisciplinary Sciences, Physcis, Others)</td><td>5.73</td><td>North America</td><td>7.04</td></tr><tr><td></td><td></td><td>Oceania</td><td>5.63</td></tr><tr><td></td><td></td><td>South America</td><td>2.82</td></tr><tr><td><strong>Research topics</strong></td><td></td><td><strong>Temporal Resolution</strong></td><td></td></tr><tr><td>Water management</td><td>26.8</td><td>static</td><td>54.8</td></tr><tr><td>Energy/Carbon</td><td>15.9</td><td>dynamic</td><td>45.2</td></tr><tr><td>Water price</td><td>14</td><td></td><td></td></tr><tr><td>Agriculture</td><td>11.5</td><td></td><td></td></tr><tr><td>Climate change</td><td>7.01</td><td></td><td></td></tr><tr><td>Water pollution</td><td>6.37</td><td></td><td></td></tr><tr><td>Water scarcity</td><td>6.37</td><td></td><td></td></tr><tr><td>Water trade</td><td>5.73</td><td></td><td></td></tr><tr><td>Others</td><td>6.37</td><td></td><td></td></tr><tr><td><strong>Shocks</strong></td><td></td><td><strong>Spatial resolution</strong></td><td></td></tr><tr><td>climate change and climate change effect</td><td>39.4</td><td>administrative</td><td>68.4</td></tr><tr><td>water scarcity and crisis</td><td>31</td><td>natural</td><td>25.7</td></tr><tr><td>drought and draught effect</td><td>16.9</td><td>Both</td><td>5.92</td></tr><tr><td>environmental impacts</td><td>2.8</td><td></td><td></td></tr><tr><td>flood</td><td>4.2</td><td></td><td></td></tr><tr><td>earthquake</td><td>4.2</td><td></td><td></td></tr><tr><td>others</td><td>1.5</td><td></td><td></td></tr><tr><td><strong>WEFE nexus</strong></td><td></td><td></td><td></td></tr><tr><td>W</td><td>21.6</td><td></td><td></td></tr><tr><td>WEc</td><td>36.6</td><td></td><td></td></tr><tr><td>WE</td><td>11.8</td><td></td><td></td></tr><tr><td>WF</td><td>8.5</td><td></td><td></td></tr><tr><td>WFEc</td><td>8.5</td><td></td><td></td></tr><tr><td>WEEc</td><td>7.84</td><td></td><td></td></tr><tr><td>WEF</td><td>3.92</td><td></td><td></td></tr><tr><td>WEFEc</td><td>&lt; 2.00</td><td></td><td></td></tr></tbody></table>

## 3.7 Water as a production factor

The analysis of research content, and spatial and temporal resolutions, reveals that CGE water studies are highly heterogeneous. While thematic and regional patterns can be identified—such as a focus on water management and climate change, and a geographical concentration on Asia, particularly China—a direct comparison between studies remains challenging. This
is also true for methodological aspects, which vary significantly across articles. In CGE
models, water can appear in different places: as a commodity (final for consumers and/or
intermediate for activities) and as a production factor. Although relatively few studies have
analysed water in its commodity role, a substantial body of literature has addressed its
representation as a production factor in modelling frameworks. Thus, in a detailed
methodological review, we examined how water is formulated as a production factor. We
screened the articles for explicit descriptions of water's implementation and for the
visualisation of production trees, which schematically illustrate how water is treated in CGE
models. All production trees were redrawn into a standardised format to allow for a consistent
comparison.

A production tree shows the substitution relationships among production factors in CGE models—typically labour, capital, and other inputs such as water, land, or energy. Comparing these trees reveals the variety of ways water is modelled, often in conjunction with energy or land. Figure 11 to Figure 14 present the different types of production trees found in studies that focus exclusively on water. Figure 11 displays a generic CGE production function, where the output (XS) is produced through the linear combination of intermediate commodities (CI) and value-added inputs (labour L and capital K(W)). These inputs are nested in a constant elasticity of substitution (CES) function, where labour and capital can replace one another to a certain extent. In this generic structure, water is not explicitly modelled but is implicitly included within capital K(W). This setup can be used both in studies that explicitly include water and those that do not.

Figure 12 shows a structure where two types of raw water, W₁ and W₂ (e.g., groundwater and surface water), are treated as substitutable with each other and also with capital. This structure appears in three studies: Koopman et al. (2017), Zhang et al. (2022) and Cazcarro et al. (2020). While substitution between water types may be realistic, the substitution between water and capital is technically implausible. In specific contexts, certain technologies (e.g., water-saving devices like drop irrigation or infrastructure like pipelines) may partially reduce the need for raw water, but full substitutability remains questionable. Figure 13 illustrates a composite raw water specification, found in six studies (i.e., Briand et al., 2023; Luckmann et al., 2014; Peng et al., 2020; Tian et al., 2021; Liu et al., 2017; Luckmann et al., 2016 a,b). In this approach, groundwater and surface water are first combined into a composite water input (WC), which is then treated as substitutable with labour and capital. Although this specification is more commonly used, it introduces the same technical concern as in Figure 12 —namely, the questionable substitutability of water with labour and capital, depending on the CES elasticity values, which can be chosen as very small and thus limiting the substitutability.

Figure 14 presents a more technically robust formulation, used in only two studies: Shahpari et al. (2022) and Xin et al. (2022). Here, raw water types are first combined into a composite water input (WC). Labour (L) and capital (K) are also combined into a separate composite of primary production factors. The model then allows substitution between the WC and the L-K composite. This structure closely mirrors technical reality, especially when the elasticity of substitution is kept low, reflecting that water cannot easily replace labour or capital. Table 2 provides an overview of the different types of production trees identified across 43 studies. In total, we find 22 distinct production tree specifications, including CGE models that also incorporate land and energy alongside water. A complete display of all production trees can be found in Annex 7.3.4.

However, the true number of possible specifications likely exceeds those found, as a wide
range of theoretical structures could be developed to model water (especially in combination
with land and energy). The multiple ways in which water is specified as a production factor
illustrates the flexibility of CGE modelling. Yet, this variation also makes it difficult to compare studies directly. Even with the same CGE model, results may differ significantly depending on the structure of the production function.
Table 3 lists all identified production function types along with the corresponding studies in which they were applied.

<figure><img src="image_13.png" /><figcaption>Figure 11: Generic production tree with water implicitly considered (Type W 00)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Figure 13: Water in a water composite nested with labour and capital separately (Type W 02)</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure 12: Water in a composite capital-water (Type W 01)</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Figure 14: Water in a water composite nested with a labour capital composite (Type W 03)</figcaption></figure>

Notes: XS = output of production; CI = intermediate commodities as input actor; KL(W) = value added resulting from labour L and capital K and water (W) aggregated within capital water (W); KLW = value added resulting from labour L and capital K and water W; L = labour L as primary production factor or as composite of different labour types; K(W) = capital K including water (W) aggregated within capital; KW = composite of capital K including water W and different water types W; L = labour L as primary production factor or as composite of different labour types; K = capital K as single capital type or composite of different capital types; W1 = raw water type 1 as a production factor (e.g., groundwater); W2 = raw water type 2 as a production factor (e.g., surface water); WC = raw water composite as a production factor nesting different water types; LK = value added resulting from labour (L) and capital (K); W = raw water or composite of different water types;

<table><caption>Table 2: Number of production tree types and number of studies</caption><thead><tr><td></td><td>Number of types</td><td>Number of studies</td></tr></thead><tbody><tr><td>Water (W) only water</td><td>4</td><td>15</td></tr><tr><td>Water (W) and Land (T)</td><td>12</td><td>17</td></tr><tr><td>Water (W) and Energy (E)</td><td>4</td><td>9</td></tr><tr><td>Water (W) and Land(f) and Energy (E)</td><td>2</td><td>2</td></tr><tr><td>Total</td><td>22</td><td>43</td></tr></tbody></table>
<table><caption>Table 3: Production tree types with the production factors water, land and energy and number of studies</caption><thead><tr><td>Type</td><td>Description</td><td>Studies</td><td>Number of studies (n = 43)</td></tr></thead><tbody><tr><td>Type W 00</td><td>water as natural resource capital implicitly represented an aggregated production factor capital</td><td>generic</td><td></td></tr><tr><td>Type W 01</td><td>labour-capital-water VA with labour and capital-water-composite</td><td>Koopman et al. (2017), Zhang et al. (2022), Cazcarro et al. (2020)</td><td>3</td></tr><tr><td>Type W 02</td><td>labour-capital-water VA with labour and capital and water-composite</td><td>Briand et al. (2023), Luckmann et al. (2014), Peng et al. (2020), Tian et al. (2021), Liu et al. (2017), Luckmann et al. (2016a,b)</td><td>7</td></tr><tr><td>Type W 03</td><td>labour-capital-water VA with water-composite and labour-capital-composite</td><td>Shahpari et al. (2022), Xin et al. (2022)</td><td>2</td></tr><tr><td>Type W 04</td><td>water types as intermediate commodity</td><td>Ma et al. (2022), Peng et al. (2020), Gao et al. (2021)</td><td>3</td></tr><tr><td>Type T 00</td><td>labour-capital-land-water VA with a composite labour and capital and aggregated land-water</td><td>Nechifor &amp; Winning, 2017</td><td>1</td></tr><tr><td>Type T 01</td><td>water and land as intermediate commodities</td><td>Li et al. (2015)</td><td>1</td></tr><tr><td>Type T 02</td><td>water-composite as intermediate commodity, labour-capital-land VA with a composite of labour and composite and land</td><td>Lin et al. (2022), Qu et al. (2022), Lin et al. (2021)</td><td>3</td></tr><tr><td>Type T 03</td><td>labour-capital-land-water VA with labour and capital and land-water-composite</td><td>Strzepek et al. (2008)</td><td>1</td></tr><tr><td>Type T 04</td><td>capital-labour VA and land-water VA composed of land-water composites</td><td>Zhang et al. (2020), Li et al. (2018)</td><td>2</td></tr><tr><td>Type T 05</td><td>capital-labour-land-water VA with capital and land and land-water composites consisting of land-and water type composites</td><td>Osman et al. (2019), Zeshan &amp; Shakeel, 2020), Wu et al. (2014)</td><td>3</td></tr><tr><td>Type T 06</td><td>capital-labour-land-water VA with water composite and capital-labour-land composite consisting of labour and capital and land-tpyes composite</td><td>Zhan et al. (2015)</td><td>1</td></tr><tr><td>Type T 07</td><td>water as intermediate commodity labour-capital-land VA with labour-capital-land composite consisting of labour and capital and land-types composite</td><td>van Heerden et al. (2008)</td><td>1</td></tr><tr><td>Type T 08</td><td>output as composite of water and VA composed of intermediate commodity and labour-capital-land VA as a composite of labour and capital and land</td><td>Peterson et al. (2005)</td><td>1</td></tr><tr><td>Type T 09</td><td>capital-labour-land-water VA composed of labour and capital-land-water composite with capital and land-water type composites</td><td>Lennox &amp; Diukanova (2011)</td><td>1</td></tr><tr><td>Type T 10</td><td>capital-labour-land-water VA composed of water and capital-land composite with capital and land-type composites</td><td>Sánchez Chóliz &amp; Sarasa (2019)</td><td>1</td></tr><tr><td>Type T 11</td><td>output composed of intermediate consumption and labour-capital-composite and land-water composite consisting of linearly compined land and water and CES combined landtype with landtype-water capital</td><td>Li et al. (2015)</td><td>1</td></tr><tr><td>Type E 00</td><td>energy as intermediate commodity</td><td></td><td></td></tr><tr><td>Type E 01</td><td>energy-labour-capital-water VA with energy-capital-labour composite and energy-capital-water composite</td><td>Teotónio et al. (2020)</td><td>1</td></tr><tr><td>Type E 02</td><td>water as intermediate commodity and energy-capital-labour VA composed of capital and energy</td><td>Sun et al. (2021), Sun et al. (2021), Sun et al. (2021)</td><td>4</td></tr><tr><td>Type E 03</td><td>energy-labour-capital-water VA with energy composite and capital-labour-water composite composed of labour and capital-water composite</td><td>Sun et al. (2021), Gao et al. (2021), Sun &amp; Sheng, 2022)</td><td>3</td></tr><tr><td>Type E 04</td><td>output composed of water and capital-labour-energy composite with capital-labour composite and energy-composite</td><td>Rivers &amp; Groves (2013)</td><td>1</td></tr><tr><td>Type TE 01</td><td>energy-capital-labour-land-water VA composed of labour and capital-energy composite and land-composites and land-water-composite</td><td>Kahsay et al. (2019)</td><td>1</td></tr><tr><td>Type TE 02</td><td>Type TE 02: capital as other production factor, labour-energy-labour-land-water VA composed of labour and energy-water composite</td><td>Vöhringer et al. (2019)</td><td>1</td></tr></tbody></table>
# 4 Discussion

## 4.1 Research scopes

The rapid increase in CGE-water publications after 2010 demonstrates the growing recognition of these models as suitable tools for capturing the complex interlinkages between water resources and economic systems. This growth has occurred across economics, environmental science, and earth and planetary sciences, which underscore the model's utility for analysing cross-sectoral impacts. The broad range of disciplinary coverage illustrates a promising approach to apply economic modelling for and practical water governance issues (Lennox & Diukanova, 2011; Dixon et al., 2011; Sartori et al., 2024; Basheer, 2024; Tian et al., 2022; Sun et al., 2021; Jin et al., 2019; Letsoalo et al., 2007; Qin, et al., 2013; Jin et al., 2014).

Although topics such as climate change, water scarcity, and water resource management dominate the literature, several critical areas remain underexplored. Household-level impacts—such as water access, affordability, and distributional effects—receive relatively little attention (Juana et al., 2008; Rivers & Groves, 2013), even though they are central to questions of equity and welfare. Likewise, floods and pollution, despite their major economic and social consequences are rarely addressed within CGE frameworks. This thematic imbalance suggests that CGE research has prioritised systemic, aggregate shocks over specific shocks and their impacts. As a result, the literature may underestimate the heterogeneity of water-related impacts across different social groups and regions, thereby limiting its usefulness for designing inclusive and context-sensitive policy interventions.

The integration of CGE models into the Water-Energy-Food-Ecosystems (WEFE) nexus provides a promising pathway for holistic assessments of resource interdependencies. However, only a limited number of studies consider interactions across multiple nexus pillars (Castelli et al., 2024; Lucca et al., 2023). As global policy frameworks increasingly demand cross-sectoral strategies, the underuse of CGE in nexus applications highlights the research gap for future model development.

Geographically, the dominance of Asian contributions—particularly from China—contrasts with the limited attention given to Africa and other vulnerable regions. While this imbalance partly reflects disparities in data availability and research capacity, it also risks producing a skewed evidence base that does not adequately reflect the global distribution of climate and water risks. For example, South Africa is relatively well studied, yet other Sub-Saharan African countries, despite being highly exposed to climate-driven water stress (e.g., Leal Filho et al., 2022), are almost absent from the literature (Dinar, 2024; Liu et al., 2016; Hertel & Liu, 2019). This gap suggests that the places most affected by water insecurity are the least represented in CGE-based analyses, limiting the relevance of current findings for global policy debates.

A similar gap is visible in the treatment of natural regions: few studies employ river basins or hydrological boundaries, despite their suitability for representing water dynamics more realistically. The continued reliance on administrative boundaries reflects data constraints, but it narrows the ecological relevance of the findings. Recent advances in regional SAM construction, such as those developed for European NUTS2 regions (García-Rodríguez et al., 2025, 2023). Regional SAM represent a promising avenue: if hydrological and administrative boundaries overlap sufficiently, SAM-based CGE models can approximate river basin regions. Appropriate compromise between modelling with administrative regions and precisely representing hydrological regions can be a key for data access for regional water CGE modelling.
Overall, while CGE applications to water have diversified, significant gaps remain in household impacts, WEFE nexus implementation, and regional representation. Addressing these blind spots will require further development of data base and interdisciplinary collaboration.

## 4.2 Production tree

One of the distinctive features of CGE-water modelling is the wide range of production function specifications. This flexibility is a strength, as it allows researchers to tailor models to different policy questions and contexts (Luckmann et al., 2014; Briand et al., 2023; Faust et al., 2015; Ma et al., 2022; Qu et al., 2022; Qin et al., 2013; Osman et al., 2019; Vöhringer et al., 2019; Peng, et al., 2020). However, it also introduces comparability problems and, in some cases, methodological weaknesses.

In particular, several specifications assume implausible substitution possibilities—such as treating water as interchangeable with labour or capital (Qin et al., 2013; Qin et al., 2012; Zhang et al., 2022; Koopman et al., 2017; Smajgl et al., 2009; Cazcarro et al., 2020; Qin et al., 2012; Gomez et al., 2004; Lennox & Diukanova, 2011; Peng, et al., 2020).

Although some capital investments (e.g., efficient irrigation) can partially reduce water demand (Philip et al., 2014; Calzadilla et al., 2011; Sánchez Chóliz & Sarasa, 2019; van Heerden et al., 2008), water generally plays a non-substitutable role in production. The same caution applies to other natural resources, such as land (Zhong et al., 2015; Feng et al., 2007; Strzepek et al., 2008; Xiang et al., 2023; Zeshan & Shakeel, 2020; Liu et al., 2017; Zhang et al., 2020).

Another limitation is that CGE models often treat water scarcity as a price effect: reduced availability raises the cost of water inputs. While this approach reflects many real-world cases (e.g., deeper wells requiring higher investment) (Gomez et al., 2004; Teotónio et al., 2020; Diao et al., 2008; Peng, et al., 2020) it breaks down when water becomes absolutely unavailable. In such cases, models may still simulate rising prices, while in reality supply would simply cease. Recognising this boundary is critical for ensuring realistic scenario design, particularly under conditions of extreme scarcity.

## 4.3 Contribution of this review

This review complements the earlier surveys of CGE-water modelling, e.g., Dinar (2014), Dudu & Chumi (2008), Johansson (2005), Bekchanov et al., (2017), Calzadilla et al. (2016), Hertel & Liu (2019), Griffith (2012), Ponce & Giupponi (2012). Unlike most previous reviews—which were either descriptive or limited in scope—this study provides a systematic and reproducible synthesis of 157 studies, extending coverage to 2025. Beyond cataloguing modelling approaches, it interrogates structural challenges, particularly in the design of production trees, which remain central to how water is represented within CGE frameworks. Recent reviews have increasingly situated water within the WEFE nexus (e.g., Bardazzi & Bosello, 2021; Raviv et al., 2022; Castelli et al., 2024; Dinar, 2024) but all point to persistent fragmentation and a lack of standardisation. By offering a detailed methodological classification and mapping thematic and geographic biases, our review provides a foundation for advancing toward greater comparability, interdisciplinary integration, and policy relevance in future CGE-water research.

# 5 Conclusions

This review demonstrates the rapid expansion of Computable General Equilibrium (CGE) models applied to water over the past 25 years, with a sharp rise after 2010. While this growth
confirms the relevance of CGE for analysing water–economy linkages, it also reveals persistent gaps in scope, methods, and geography.

Water CGE studies are concentrated on Asia—particularly China—while regions highly vulnerable to water stress, such as Sub-Saharan Africa, receive only limited coverage. Similarly, natural hydrological boundaries, such as river basins, are rarely modelled, despite their greater ecological realism. These imbalances limit the global applicability of CGE-water research and call for stronger data collection and modelling for underrepresented regions.

Methodologically, the diversity of production function specifications shows both the flexibility and the inconsistency of current practice. Some common assumptions—such as treating water as a substitute for labour or capital—are technically questionable. Progress will depend on developing more standardised yet realistic approaches, supported by systematic model comparisons.

Looking forward, three priorities emerge for advancing the field: (i) Methodological consolidation – clearer standards for representing water as a production factor, alongside systematic testing of alternative specifications. (ii) Geographic and thematic expansion – especially towards, regions facing water stress, household-level impacts, water affordability, floods, and pollution, which remain marginal in the literature. (iii) Interdisciplinary integration – stronger linkages between CGE models, hydrological data, and nexus frameworks such as the Water-Energy-Food-Ecosystems (WEFE).

By addressing these priorities, CGE-water modelling can move beyond fragmented applications and contribute more directly to emerging strategies such as the European Water Resilience Strategy resource (European Commission, 2025). The strength of CGE lies in its capacity to represent diverse agents, sectors, and policy levers within a single consistent framework. Realising this potential requires methodological rigour, broader regional representation, and closer dialogue with policymakers seeking integrated, equitable, and resilient water governance. Thus, the Water-CGE model constitutes fruitful analytical frameworks for assessing alternative scenarios and water policies that advance a “whole-of-society” approach to water governance (European Commission, 2025) while simultaneously supporting progress toward universal access to clean water and sanitation, and the preservation of aquatic ecosystems (United Nations, 2025).

## Funding

This work was supported by the European Union's Horizon EUROPE research and innovation program [grant number: 101086512]; and the Région Normandie.

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# 7 Appendix

## 7.1 Introduction

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<table><caption>Table A-1-1: Comparative review of CGE models applied to water and related methodologies</caption><thead><tr><th>Study</th><th>Cover period</th><th>CGE Articles Analysed</th><th>Review Method Classification by Grant &amp; Booth (2009)</th><th>WEFE nexus</th><th>Specification of production functions / Modelling</th><th>Specification of Timel resolution</th><th>CGE-water Framework</th><th>Quantitative Analysis of articles</th></tr></thead><tbody><tr><td rowspan="2">Johansson (2005)</td><td>1985-2002</td><td>8</td><td>Overview</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>2000-2007</td><td>20</td><td>State-of-the-art review</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td></tr><tr><td rowspan="2">Dudu &amp; Chumi (2008)</td><td>2008</td><td>3</td><td>Overview</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td></tr><tr><td>2000-2012</td><td>NA (≈ 25-30)</td><td>State-of-the-art review</td><td>N</td><td>Y</td><td>N</td><td>N</td><td>N</td></tr><tr><td rowspan="2">Brouwer &amp; Hofkes (2008)</td><td>1990 - 2012</td><td>80 (published and unpublished)</td><td>Mapping Review / systematic map</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>N</td></tr><tr><td>1990-2011</td><td>NA (≈ 15))</td><td>Literature review</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td rowspan="2">Ponce &amp; Giupponi (2012)</td><td>2000-2011</td><td>56</td><td>State-of-the-art review</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td></tr><tr><td>2000-2014</td><td>NA (≈ 35)</td><td>Mapping review/ systematic map</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td></tr><tr><td rowspan="2">Fadali, et al. (2012)</td><td>1995-2015</td><td>42</td><td>Systematic review</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>Y</td></tr><tr><td>≈ 1998- 2014</td><td>NA (≈ 40-45))</td><td>Critical Review</td><td>N</td><td>Y</td><td>N</td><td>Y</td><td>N</td></tr><tr><td rowspan="2">Griffith (2012)</td><td>2016-2018</td><td>6</td><td>Literature review</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>≈ 2000- 2021</td><td>90</td><td>Systematic review</td><td>Y</td><td>Y</td><td>Y</td><td>N</td><td>Y</td></tr><tr><td rowspan="2">Dinar (2014)</td><td>≈ 2010- 2021</td><td>NA</td><td>State-of-the-art review</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>2002-2021</td><td>27</td><td>Mapping review / systematic map</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr><tr><td rowspan="2">Calzadilla et al. (2016)</td><td>NA</td><td>NA</td><td>State-of-the-art review</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>1995-2025</td><td>157</td><td>Systematic review</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>
<table><caption>Table A-1-2: Comparative review of CGE models applied to water and related methodologies</caption><thead><tr><th>Study</th><th>Cover period</th><th>CGE Articles Analysed</th><th>Review Method Classification by Grant &amp; Booth (2009)</th><th>Objective</th><th>Spatial resolution</th><th>Model family</th><th>WEF E nexus</th><th>Specification of production functions / modelling</th><th>Specification of time resolution</th><th>CGE-water Framework</th><th>Quantitative Analysis of articles</th><th>Method collected articles</th><th>Key gaps identify</th><th>Updates earlier review</th></tr></thead><tbody><tr><td>Johansson (2005)</td><td>1985 - 2002</td><td>8</td><td>Overview</td><td>Integrated Approaches to Water Valuation</td><td>Global</td><td>CGE/ Econometric/ Mathematical Programming</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>Examination of water valuation methods / data availability / asses the link between micro and macro policy interventions</td><td>First</td></tr><tr><td></td><td>2000 - 2007</td><td>20</td><td>State-of-the-art review</td><td>Irrigation water management issues.</td><td>Global</td><td>CGE/PE(partial equilibirum</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td><td>NA</td><td>Lack of different pricing methods/ uncertainty due to climate change not taken into account by the models/ market failures are at the heart of water policy under represented</td><td>Johansson, (2005)</td></tr><tr><td>Dudu &amp; Chumi (2008)</td><td>2008</td><td>3</td><td>Overview</td><td>Key issues and future research directions in integrated hydro-economic modelling</td><td>National, basin</td><td>Holistic / Modular / CGE</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td><td>NA</td><td>Heterogenous modeling paradigms between simulation and optimization techniques / spatial unit mismatch / temporal step mismatch</td><td>NA</td></tr><tr><td></td><td>2000 - 2012</td><td>NA (≈ 25-30)</td><td>State-of-the-art review</td><td>State of art CGE approaches for water issues at global/nationa</td><td>Global and national scale</td><td>CGE</td><td>N</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>Lack of disaggregated data (industry, agricultural sub-sectors), obsolete price</td><td>NA</td></tr></tbody></table>
<table><tr><td>Brouwer & Hofkes (2008)</td><td>1990 - 2012</td><td>80 (published and unpublished)</td><td>Mapping Review / systematic map</td><td>A review of all types of water CGE models that appear in the literature</td><td>Global, national, multi-regional</td><td>CGE</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>N</td><td>Three databases search: Econlit, Web of Knowledge and Google Scholar</td><td>elasticities, often simplified water specifications</td><td>NA</td></tr><tr><td></td><td>1990 - 2011</td><td>NA (≈15))</td><td>Literature review</td><td>CGE Modeling of Water Resources</td><td>Global</td><td>CGE</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>Gobal CGE under represents sub-regional dynamics</td><td>NA</td></tr><tr><td>Ponce & Giupponi (2012)</td><td>2000 - 2011</td><td>56</td><td>State-of-the-art review</td><td>Water policy impacts in economy-wide context</td><td>Global</td><td>CGE (micro-macro link)</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td><td>NA</td><td>Methodological heterogeneity / missing the link between micro and macro policy interventions</td><td>Johansson (2005), Dudu & Chumi (2008)</td></tr><tr><td></td><td>2000 - 2014</td><td>NA (≈ 35)</td><td>Mapping review/systematic map</td><td>CGE models accounting for water issues</td><td>Global/national</td><td>CGE</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>N</td><td>NA</td><td>Analysis limited to agricultural water use/ inclusion of non-consumptive uses / heterogeneity among the water-using agents or regions</td><td>Johansson (2005), Dudu & Chumi, (2008), Dinar (2014)</td></tr><tr><td>Fadali et al. (2012)</td><td>1995 - 2015</td><td>42</td><td>Systematic review / Umbrella Review ?</td><td>To classify compare WEMs (Water-economy</td><td>Global, Basinwide, Nationwide, city, country</td><td>SIMOP, IOM, CGE</td><td>N</td><td>Y</td><td>Y</td><td>N</td><td>Y</td><td>Multi-database search (Web of Science, Hydrological dynamics ignored / insufficient spatiotemporal</td><td>Cai (2008), Brouwer & Hofkes (2008),</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td>models)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>ProQuest, CAB direct...) + screening, deduplication and coding</td><td>disaggregation, interlinkages between the water, energy, and food sectors and insufficient assumption justification/ greater need for multidisciplinarity research</td><td>Booker et al. (2012), Harou et al. (2009), Dinar (2012)</td></tr><tr><td></td><td>≈ 1998 - 2014</td><td>NA (≈ 40-45))</td><td>Critical Review</td><td>Water scarcity impact in CGE</td><td>Global/Multi-region</td><td>CGE</td><td>N</td><td>Y</td><td>N</td><td>Y</td><td>N</td><td>NA</td><td>Cross-scale data gap / transboundary modeling gap / return-flow omission</td><td>NA</td></tr><tr><td>Griffith (2012)</td><td>2016 - 2018</td><td>6</td><td>Litterature review</td><td>Map and describe the concepts, research focuses, mechanisms, and methodologies in the energy-carbon-water (ECW) nexus</td><td>Global</td><td>LCA (Life Cycle Analysis) / IO model / CGE / System dynamic model / Optimization methods / MFA (Material flow analysis</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>Structural limitation of CGE to represent a multi-sector ECW nexus</td><td>NA</td></tr><tr><td></td><td>≈ 2000 - 2021</td><td>90</td><td>Systematic review / Umbrella Review ?</td><td>Water-Energy-Food Nexus in CGE</td><td>Global/national/Multi-region</td><td>CGE</td><td>Y</td><td>Y</td><td>Y</td><td>N</td><td>Y</td><td>systematic review (PRISMA 2009)</td><td>Water model realism gap / Water-Energy is under represented compared to other nexus component / lack of analysis of residential water-demand dynamics / transboundary scale mismatch</td><td>NA</td></tr><tr><td>Dinar (2014)</td><td>≈ 2010 - 2021</td><td>NA</td><td>State-of-the-art review</td><td>Economic Impact of Water Availability on Food</td><td>Global/ Mediterranean Sea basin</td><td>CGE</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>The focus is rarely on alternative water sources / water sources</td><td>NA</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td>Security and the Related Ecosystems</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>aggregation gap /Ecosystem externality omission (lack of comprehensive WEFE analysis / micro-macro ecosystem linkage remains challenging for cge analysis</td><td></td></tr><tr><td></td><td>2002 - 2021</td><td>27</td><td>Mapping review / systematic map</td><td>Macroeconomic models in the water, energy, food, and ecosystem (WEFE)</td><td>Global</td><td>CGE/IAMs/ABMs/DS GE</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Data search + citation</td><td>Scarce harmonised datasets; new WEFE data-collection, absence of a common framework and storage protocols required / calibration-sensitive predictive loss /interdisciplinarity gap</td><td>McCarl et al. (2017a,b), Albrecht et al. (2018), Endo et al. (2020)</td></tr><tr><td>Calzadilla et al. (2016)</td><td>NA</td><td>NA</td><td>State-of-the-art review</td><td>Food-Water-Ecosystem Challenges</td><td>Global</td><td>Experimental Economics / Game Theory / Hydro-Economic Modeling / CGE</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>Absence of standardized data / transboundary basin overlaps /jurisdictional water data gaps</td><td>Calzadilla et al. (2016), Bardazzi & Bosello, (2021)</td></tr><tr><td></td><td>1995 - 2025</td><td>157</td><td>Systemic review</td><td>Water issues and modeling in CGE</td><td>Global, national, sub-national and natural</td><td>CGE</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Systemic review PRISMA 2020 guidelines (Page et al., 2021).</td><td>NA</td><td>Johansson (2005), Dudu & Chumi, (2008), Ponce & Giupponi, (2012), Griffith (2012), Calzadilla, et al.</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(2016),<br>Dinar,<br>(2014)</td></tr></table>
## 7.2 Methodology

### 7.2.1 Search operators in Web Of Science

We used following key-word request to identify CGE model studies with relevance for water and excluding false positive records:

Search operators defined

Years: 1989 to 2025; Publication type: Journal Articles

TI=("CGE" NOT "crude gut extract" NOT "cold gas efficiency" NOT "capillary gel electrophoresis" NOT "carbon gasification efficiency" NOT "cold gas efficiency" NOT "glassy carbon electrode" NOT "CGE/" NOT "/CGE" AND "water" OR "computable general equilibrium" AND "water") OR AB=("CGE" NOT "crude gut extract" NOT "cold gas efficiency" NOT "capillary gel electrophoresis" NOT "carbon gasification efficiency" NOT "cold gas efficiency" NOT "glassy carbon electrode" NOT "CGE/" NOT "/CGE" AND "water" OR "computable general equilibrium" AND "water") OR KP=("CGE" NOT "crude gut extract" NOT "cold gas efficiency" NOT "capillary gel electrophoresis" NOT "carbon gasification efficiency" NOT "cold gas efficiency" NOT "glassy carbon electrode" NOT "CGE/" NOT "/CGE" AND "water" OR "computable general equilibrium" AND "water")

## 7.3 Results

### 7.3.1 Research disciplines

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### 7.3.2 Research topics

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### 7.3.3 Study regions and temporal scope

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### 7.3.4 Water as a production factor

#### 7.3.4.1 Presentation of different types of production tree in CGE models

*Production trees with only water*

<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

C = intermediate commodities

Wn = water as intermediate commodity in different water types W₁ and , W₂, Wn (e.g., piped water, industrial water, ...)

LKTW = value added (VA) resulting from labour (L) capital (K), land-water factor (TW)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

TW = land-water factor, representing land which on which water is used or available (e.g., irrigated agricultural land, wet-lands, ...)

<figure><img src="image_19.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

W = water as production factor

T = land as production factor

LK = value added (VA) resulting from labour (L) capital (K)

L = labour single primary production factor, or as a composite of different labour types

K = capital as a primary production factor

Type T 00: labour-capital-land-water VA with a of composite labour and capital and aggregated land-water, e.g., in Nechifor & Winning (2017)

<figure><img src="image_20.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

C = intermediate commodities

Wn = water as intermediate commodity in different water types W₁ and , W₂, Wn (e.g., piped water, industrial water, ...)

LKT = value added (VA) resulting from labour (L) capital (K) and land (T)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

Type T 01: water and land as intermediate commodities, e.g., in Li et al. (2015)

<figure><img src="image_21.png" /></figure>

Legend

XS = output of production

CI = composite intermediate input

C = other intermediate input commodities

C = intermediate commodities

Wn = water as intermediate commodity in different water types W₁ and , W₂, Wn (e.g., piped water, industrial water, ...)

LKT = value added (VA) resulting from labour (L) capital (K) and land (T)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

Type T 02a: water( types) as intermediate commodities, labour-capital-land VA with a composite of labour and land, similar to Type T 02b

Type T 02b: water-composite as intermediate commodity, labour-capital-land VA with a composite of labour and composite and land, e.g. in: Lin et al. (2021), Lin et al. (2022), Qu et al. (2022).
<figure><img src="image_22.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

LKTW = value added (VA) resulting from labour (L), capital (K), land-water factor (TW)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

TW = land-water factor, representing land which on which water is used or available

T = land

W = water

<figure><img src="image_23.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

TW = land (T) water (W) composite

T = land as production factor

W = water as production factor

LK = value added (VA) resulting from labour (L) capital (K)

L = labour single primary production factor, or as a composite of different labour types

K = capital as a primary production factor

Type T 03: labour-capital-land-water VA with labour and capital and land-water-composite, e.g. in: Strzepek et al. (2008)

<figure><img src="image_24.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

KL = value added (VA) from capital (K) and labour (L)

TW = composite of land (T) and water (W)

T_n = landtype (T) which composes with specific water usages (W_n), e.g., irrigated land, industrial land

W_n = water as an production factor in different water types which compose with land types Tn, e.g., irrigation water, industrial water.

Type T 04: land-water VA and labour-capital VA, not found but possible

<figure><img src="image_25.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

KL = value added (VA) from capital (K) and labour (L)

TW = composite of landtpyes (T_n) and water types (W_n)

T_n = landtype (T) which composes with specific water usages (W_n), e.g., irrigated land, industrial land

W_n = water as an production factor in different water types which compose with land types Tn, e.g., irrigation water, industrial water.

Type T 04: capital-labour VA and land-water VA composed of land-water composites, e.g. in: Li et al. (2018), Zhang et al. (2020)

Type T 05: capital-labour-land-water VA with capital and land and land-water composites consisting of land-and water type composites, e.g. in: Osman et al. (2019), Wu et al. (2014), Zeshan & Shakeel (2020)
<figure><img src="image_26.png" /></figure>

Legend

XS = output of production

CI = intermediate input

KLTW = value added (VA) resulting from capital (K), labour (L) land (T) and water (W)

KLT = composite of labour (L), capital (K) and land (TC)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

TC = composite of land with different land types

T<sub>n</sub> = landtype which composes with specific water usages, e.g., irrigated land, industrial land

Type T 06: capital-labour-land-water VA with water composite and capital-labour-land composite consisting of labour and capital and land-types composite, e.g. in: Zhan et al. (2015)

<figure><img src="image_27.png" /></figure>

Legend

XS = output of production

W = water

CI = intermediate input

CKLT = value added (VA) resulting from intermediate input (CI), labour (L) capital (K), land (T)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

T = land

Type T 08: output as composite of water and VA composed of intermediate commodity and labour-capital-land VA as a composite of labour and capital and land, e.g. in: Peterson et al. (2005)

<figure><img src="image_28.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

W = water as production factor

LKT = value added (VA) resulting from labour (L) capital (K) and land (T).

L = labour single primary production factor, or as a composite of different labour types

K = capital as a primary production factor

Type T 07: water as intermediate commodity labour-capital-land VA with labour-capital-land composite consisting of labour and capital and land-types composite, e.g. in: van Heerden et al. (2008)

<figure><img src="image_29.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

C = intermediate commodities

LKTW = value added (VA) resulting from labour (L) capital (K), land-water factor (TW)

L = labour as single primary production factor, or as a composite of different labour types

KTW = composite of capital (K), land (T) and water (W)

W<sub>n</sub> = water as production factor single or in different water types W<sub>1</sub> and , W<sub>2</sub>, W<sub>n</sub> (e.g., groundwater, surface water, ...)

KT = composite of capital (K) and land (T)

K = capital (K) a single primary production factor or as composite of different capital types

TC = composite of land

T<sub>n</sub> = different land types (e.g., irrigated land, rainfed land, ...)

Type T XX: labour-capital-land-water VA composed of labour and capital-land-water composite with water and capital-land composite. Not found but possible
<figure><img src="image_30.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

C = intermediate commodities

KLTW = value added (VA) resulting from capital (K), labour (L) land (T) and water (W)

L = labour as single primary production factor, or as a composite of different labour types

KTW = composite of capital (K), land (T) and water (W)

K = capital (K) a single primary production factor or as composite of different capital types

TW = land (T) and water (W)

Tn = landtype (T) which composes with specific water usages (Wn), e.g., irrigated land, industrial land

Wn = water as an production factor in different water types which compose with land types Tn, e.g., irrigation water, industrial water.

<figure><img src="image_31.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

C = intermediate commodities

KLTW = value added (VA) resulting from capital (K), labour (L) land (T) and water (W)

L = labour as single primary production factor, or as a composite of different labour types

KTW = composite of capital (K), land (T) and water (W)

K = capital (K) a single primary production factor or as composite of different capital types

TW = land (T) and water (W)

Tn = landtype which composes with specific water usages, e.g., irrigated land, industrial land

Type T 09: capital-labour-land-water VA composed of labour and capital-land-water composite with capital and land-water type composites, e.g. in: Lennox & Diukanova (2011)

<figure><img src="image_32.png" /></figure>

Legend

XS = output of production

CI = intermediate consumption

LK = value added from labour (L) and capital (K)

L = labour as single primary production factor, or as a composite of different labour types

K = capital (K) a single primary production factor or as composite of different capital types

TW = composite of land (T) and water (W)

T₁W₁ = industrial land (T₁) and industrial water (W₁)

T₂W₂ = agricultural pasture land (T₂) irrigated land (T₃) and irrigation water (W₃)

Type T 11: output composed of intermediate consumption and labour-capital-composite and land-water composite consisting of linearly compined land and water and CES combined landtype with landtype-water capital, e.g. in: Li et al. (2015)

Type T 10: capital-labour-land-water VA composed of water and capital-land composite with capital and land-type composites, e.g. in: Sánchez Chóliz & Sarasa (2019)
Evolution of research articles over time and the research topics

<figure><img src="image_33.png" /><figcaption>Figure A-1: Evolution of research articles over time and the research topics (12 class)</figcaption></figure>

**7.4 Discussion**

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**7.5 Conclusions**

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**7.6 References**

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