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Estimated Impacts of Smart Water Meter Implementation on Domestic Hot Water Consumption and Related Greenhouse Gas Emissions from Case Studies

Department of Civil and Environmental Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Department of Construction Environment Research, Land and Housing Institute, Daejeon 34047, Republic of Korea
School of Civil and Environmental Engineering, Kookmin University, Seoul 02707, Republic of Korea
Water Environment Center, Korea Conformity Laboratories, Seoul 08503, Republic of Korea
Authors to whom correspondence should be addressed.
Water 2023, 15(17), 3045;
Submission received: 16 July 2023 / Revised: 9 August 2023 / Accepted: 22 August 2023 / Published: 25 August 2023


This study investigates the water–energy–carbon (WEC) nexus in cities across four countries, namely the United Kingdom (UK), the United States of America (USA), Australia (AUS), and South Korea (KOR), over a decade, from 2011 to 2021. The primary objective is to assess the impact of smart water metering (SWM) implementation on the WEC nexus, with a specific focus on domestic hot water (DHW) consumption and associated greenhouse gas (GHG) emissions. The analysis of the collected data reveals diverse patterns among cities with varying levels of SWM implementation. Notably, cities with higher SWM implementation demonstrated significant reductions in water consumption, indicating the effectiveness of the efficient water consumption and demand management achieved through SWM. The study emphasizes the importance of addressing GHG emissions related to water heating, with the carbon intensity of water heating identified as a critical factor in this context. To achieve net reductions in GHG emissions, intensive efforts are required to simultaneously decrease both DHW consumption and the carbon intensity of water heating. The research findings highlight the potential for substantial GHG emissions reductions by combining SWM implementation with the decarbonization of water heating. By recognizing the interdependencies within WEC systems, this study underscores the significance of SWM in advancing toward a carbon-neutral society. In conclusion, this study contributes valuable insights into the WEC nexus and emphasizes the role of SWM in achieving sustainability goals. It advocates for integrated policies to effectively address the interconnected issues of the WEC nexus for effective climate change mitigation.

1. Introduction

The global trend of urbanization is rapidly accelerating, with over half of the world’s population currently residing in urban areas [1]. This trend is projected to persist, with estimations suggesting that by 2050, more than two-thirds of the world’s population will be living in cities, resulting in a more than twofold increase in urban population size [2].
Urbanization significantly contributes to climate change due to the increased resource consumption in urban areas compared to rural areas, resulting in elevated GHG emissions [3]. To mitigate the impacts of climate change, it is crucial to prioritize the implementation of strategies that address the unique challenges posed by urban populations and their resource consumption patterns. Embracing integrated water and energy management approaches can help mitigate the effects of climate change and foster a more sustainable future, particularly in urban settings [4].
The water sector is one of the largest consumers of energy globally, with energy consumption occurring during the extraction, conveyance, treatment, distribution, end-use, and wastewater treatment stages [5]. Therefore, it is crucial to make efforts in reducing energy consumption within the water sector as a contribution to global climate change mitigation. The end-use section of the urban water system, involving water heating activities in both domestic and industrial sectors, is responsible for a significant portion of the energy consumption in the water sector. Studies and reports indicate that the end-use section of the urban water system has an energy intensity of over 50 kWh/m3, compared to less than 3.70 kWh/m3 in the source/conveyance section, 4.23 kWh/m3 in the treatment section, 0.58 kWh/m3 in the distribution section, and 10 kWh/m3 in the wastewater treatment section [6].
DHW constitutes an indispensable component of contemporary living, essential for routine activities such as showering, dishwashing, doing laundry, etc. [7]. The proportion of energy consumption related to DHW varies globally, with countries such as the UK, the USA, AUS, and KOR, reporting up to 25% [8], 19% [9], 24% [10], and 15% [11], respectively. Enhancing the energy efficiency of the water sector and reducing associated GHG emissions requires prioritizing measures such as improving the efficiency of water heating processes, adopting renewable energy sources, and implementing water conservation strategies.
Apart from utilizing renewable energy sources such as solar and wind power, the implementation of SWM can significantly contribute to GHG emission reduction within the water sector. Experimental investigations demonstrated that SWM has the potential to effectively promote behavioral changes among users, resulting in a notable reduction, of up to 10%, in water consumption [12]. Moreover, the combination of water-saving appliances with diverse capabilities, along with SWM, generates a synergistic impact that further enhances reductions in water consumption [13]. While water-saving appliances, rainwater collection, and greywater usage directly contribute to decreasing water consumption, SWM offers the advantage of providing real-time data on water usage, which facilitates behavioral changes among users [14,15,16]. This combination of SWM and water-saving appliances enhances the overall effectiveness of water conservation efforts, resulting in significant GHG emission reductions in the water sector.
This study aimed to assess the impact of implementing SWM on the WEC nexus. To achieve this, data from five case studies in four developed countries, with significant global impact and diverse water-related policies [17], were analyzed and examined. The primary focus was to assess the effect of SWM implementation on GHG emissions resulting from DHW consumption at an annual scale. The study, however, did not consider the influence of weather and climate on DHW consumption at hourly and daily intervals. This was mainly due to the lack of accurate tools for estimating such consumption and insufficient comprehensive data on DHW consumption patterns [18], disaggregated water end-use information [19], and energy consumption records [20]. Instead, this study proposed a method to estimate the theoretical energy required to produce DHW and the resulting GHG emissions at the city and country levels with an annual resolution.
Finally, this study aimed to improve the understanding of the variabilities in the WEC nexus by examining five case studies from 2011 to 2021, focusing on factors such as (i) total water consumption (LPCD), (ii) SWM implementation levels, (iii) the share of LPCD consumed as DHW, and (iv) GHG emissions associated with DHW consumption. To the best of the authors’ knowledge, this study represents the first attempt to focus on the influence of SWM implementation levels on total water consumption, DHW consumption, and associated GHG emissions in selected countries. The findings provide insights for integrated strategies in water and energy management, offering promising solutions to address the challenges of urbanization, resource consumption, and climate change mitigation.

2. Methodology

2.1. System Boundary

The study utilized Figure 1 to define the system boundary. Within this predetermined boundary, the GHG emissions estimation specifically targeted direct GHG emissions arising from the use of energy for water heating in residential settings.
The study period from 2011 to 2021 allows access to a comprehensive dataset spanning over a decade, ensuring sufficient data availability for robust analysis. The 10-year timeframe enables the examination of long-term trends and patterns related to SWM implementation and its impact on water consumption and demand management. Also, SWM implementation is a gradual process, and this period permits observation of the technology’s implementation and its effects on water usage patterns. Additionally, it covers a period when policy changes and interventions related to water conservation and SWM may have been implemented, allowing for the identification of correlations between policies and water consumption changes. The decade-long period ensures statistical significance by providing a substantial dataset, and it facilitates comparison with other studies, making the findings more relevant and relatable to the existing literature.

2.2. Data Collection

The data collection stage involved a three-step process. In the first step, a comprehensive search was conducted using selected keywords such as “carbon intensity”, “domestic hot water (DHW)”, “greenhouse gas (GHG)”, “smart water meter (SWM)”, “water consumption”, and water–energy–carbon “WEC” nexus. Scopus was chosen for its broader coverage of the literature in various fields compared to other databases like Web of Science, Google Scholar, and PubMed. The search was conducted from 17 June 2022 to April 2023.
In the second step, a visual examination was performed on the 158 articles obtained from the initial search to remove irrelevant articles. The examination focused on whether the objectives, methodologies, or results of the articles were related to the WEC nexus study. As a result, 77 articles and reports were positively identified and retained for further analysis, providing comprehensive coverage of all aspects of the WEC nexus.
Finally, in the third step, the content analysis technique was applied to extract data from the selected articles, organizing and categorizing them into relevant groups. Content analysis was employed to discover emerging patterns and identify key data related to the WEC nexus. The study further conducted a thematic analysis to define common themes and patterns within the data.

2.3. Impact of SWM Implementation on LPCD

This study integrates data from various locations, including the UK, the USA, AUS, and KOR, to facilitate comparisons across diverse climatic environments. The research utilizes annual data on the implementation of SWM, water consumption, and energy carbon intensity for different cities. By combining these variables in the primary dataset, the study aims to examine the inter- relationships within WEC systems. The analysis aims to explore the correlation between SWM implementation levels, LPCD, DHW consumption, and GHG emissions.

2.4. Evaluation of DHW-Based Greenhouse Gas Emissions

A literature review was conducted to gather information from existing studies. This process provided valuable reference data for estimation and facilitated an understanding of trends and patterns in DHW usage.
Policies targeting end-use water consumption have mainly emphasized replacing and upgrading water-efficient appliances rather than changing customer behavior to mitigate climate change. Some studies observed that increasing SWM implementation can decrease LPCD and, consequently, GHG emissions [21]. The variations in energy intensities across different stages of water treatment processes are summarized in Figure 1. Studies suggest that the energy required for different stages of the water treatment process can vary significantly [6,22,23].
Estimation of energy intensities, however, varies greatly from study to study and region to region. These variations in energy intensities of water treatment processes are dependent on several critical factors (i.e., location, regulations, geography, climate, availability of water, water quality standards, treatment, reuse, disposal of sewage, use of different conceptual boundaries, system definitions, availability of data, stages of the system, etc.) [24,25,26]. The end-use section is particularly noteworthy because it typically accounts for approximately 70% of the energy consumed in the entire water sector [27,28]. For example, energy use from the end-use section in the total water sector was estimated as 78% (UK) [29], 72% (USA) [18], 79% (AUS) [18], and 72% (KOR) [30], mainly due to water heating activities.
LPCD share offers some insight into DHW consumption, but it is not an absolute estimator due to the influence of sociocultural and economic factors. In this study, DHW consumption estimation is derived from reported end-use water distribution for LPCD (Figure 1) [31,32]. The main emphasis of the study is on assessing the influence of SWM implementation on total water consumption, DHW consumption, and associated GHG emissions in specific countries. Consequently, a thorough examination of all factors that might affect DHW consumption is not within the scope of this research.
This study proposes a straightforward method for estimating the theoretical energy required to produce DHW based on the average share of LPCD. This method involves using the heat transfer formula (Equation (1)) to calculate the theoretical energy (Q, kWh) needed for DHW production, as suggested in a previous study [24].
Q = mCp∙ΔT
where m (kg) is the DHW mass using a constant density of 0.998 kg/L at 20 °C, the constant heat capacity (Cp) of 1.162 × 10−3 kWh/kg/°C, and the temperature change (ΔT) from cold (13 °C) to hot (55 °C) water, which is consistent with water temperature increase observed in other studies [32]. However, it is important to note that this formula is an estimate and may vary depending on specific factors related to the water heating system (i.e., the type of water heater, water temperature, and usage patterns) [33]. Like other pertinent research, this formula does not consider potential efficiency variations and energy losses within the water heating system due to limited available data. Additionally, a fixed temperature change of ΔT = 42 °C was used throughout this study, regardless of seasonal and environmental variations [34].
Understanding the relationship between LPCD values, the energy intensity of the water system, and DHW share can help assess how water demand management options impact GHG emissions. In this study, GHG emissions are calculated using Equation (2):
GHG emissions = Q∙z
where z is a country-dependent emission factor (gCO2e/kWh) [10,35]. The most commonly used energy sources for DHW production are gas, followed by electricity, renewables, and wastes as well as a variety of fossil fuels [36]. GHG emissions were solely considered from the carbon intensity of the energy used for DHW production, omitting embodied energy.

3. Results and Discussion

3.1. Impact of SWM Implementation on LPCD

The core objective of implementing SWM systems is to enhance the efficiency and effectiveness of water distribution networks and end-use by reducing both water and energy consumption [37]. In this study, SWM served as a comprehensive term encompassing both automated meter reading and advanced metering infrastructure. According to recent studies, SWM is recognized as one of the most effective methods for achieving water savings [38]. The level of water savings achieved in SWM projects varies from 2% to 10%, depending on several factors (i.e., climate, building regulations, occupancy rates, the age of buildings, and the efficiency rating of end-use appliances) [39].
Although the data collected display diverse patterns among cities with varying levels of SWM implementation, cities with high SWM implementation exhibited larger reductions in water consumption, as shown by Figure 2.

3.1.1. Southern Water Service Area, UK

Figure 2a presents the progress of SWM implementation in the Southern Water service area from 2011 to 2021. Southern Water has implemented drive-by SWM technology [40], leading to a notable rise in SWM installations from 52.2% in 2011 to 85.6% in 2016 [41]. The LPCD trend in the Southern Water service area in the UK consistently decreased over the past decade as the SWM implementation level increased, as displayed in Figure 2a. However, due to the COVID-19 lockdown in 2020, LPCD temporarily increased. In the UK, the PR 19 study identified SWM as one of the most effective tools for reducing water consumption, citing the Southern Water case as an example of how SWM led to the reduction of 33 L in LPCD [42]. An empirical analysis also reported that metered customers’ water consumption decreased by 22% compared to unmetered customers, given an identical environment with the same incentives [43]. Despite the implementation of various water conservation measures (public education campaigns and the installation of water-efficient devices and appliances) by Southern Water, SWM remains a crucial and effective measure in cities that have achieved notable water savings [44].

3.1.2. San Francisco, USA

In order to meet the water consumption reduction targets mandated by regulatory authorities, water utilities in California implemented SWM systems [45]. Figure 2b displays the trend of San Francisco’s LPCD from 2011 to 2021, correlated with the level of SWM implementation. Starting in 2012, San Francisco intensely replaced the majority of its traditional mechanical water meters with SWM over two years [46]. The implementation of SWM further increased during the 2012–2016 drought, which mandated urban water suppliers to adopt water shortage contingency plans [47].
The huge increase in the level of SWM implementation allowed customers to monitor their water usage in real-time through an online portal and helped the city to identify leaks and promote water conservation, resulting in a reduction in LPCD from 176 to 159 L [48]. San Francisco also implemented other initiatives to conserve water, including providing rebates for water-efficient appliances and fixtures and promoting the use of recycled water for non-potable purposes such as irrigation. Despite a temporary increase in water consumption during the COVID-19 lockdown, San Francisco’s LPCD remains at around 159 L, which is among the lowest rates in California and half of the state average [49].

3.1.3. City of Sacramento, USA

The impact of SWM implementation on LPCD in the City of Sacramento is analyzed between 2015 and 2021 in Figure 2c [46,47]. The program was launched in 2015, and, within the first year, LPCD decreased from 238 to 226 L in 2016. The City of Sacramento accelerated the 8-year SWM implementation program by investing USD 245 million into the program, aimed at applying consumption-based pricing for all water customers [50]. As the SWM implementation level increased from 47% to 89%, LPCD decreased from 234 L in 2017 to 219 L in 2019. However, the COVID-19 lockdown caused an increase in LPCD, to 240 L in 2020 [51]. By 2021, LPCD reduced to 196 L, when the SWM implementation level reached 93%, coupled with the provision of real-time water consumption feedback through the WaterSmart customer portal [52].

3.1.4. Melbourne, Australia

Melbourne uses a mechanical metering system that was adopted in the 1940s and only considered implementing SWM in 2017 as a pilot study [53]. Currently, a joint SWM pilot study among City West Water, Southeast Water, and Yarra Valley Water provides daily water consumption information to about 5% of the city’s 2.2 million customers [54]. During the drought period, the LPCD of Melbourne decreased by 22% from 1996 to 2010 due to a limited water supply [55]. However, since 2011, LPCD fluctuated between 147 and 162 L through 2019, as displayed in Figure 2d [54]. To address the increasing trend in LPCD, SWM and other water conservation measures must be more widely adopted in Melbourne. As mechanical meters were only read quarterly in the city, the absence of real-time water consumption data, leak detection, repair, and immediate billing information hindered the achievement of mandated water consumption reduction targets [56].

3.1.5. Sejong City, South Korea

As part of a national initiative, Sejong City became one of the early adopters of smart water city projects in South Korea [57]. This led to a significant decrease in LPCD, which dropped from 426 L in 2012 to 270 L in 2020, as presented in Figure 2e [58]. The use of SWM technologies implemented in Sejong City supported other water conservation measures by allowing residents to monitor their water consumption in (near) real-time. However, despite the substantial decrease in LPCD, Sejong City continues to exhibit relatively high water consumption. This is largely attributed to factors such as the low water price, rapid urbanization, and industrialization trends in South Korea [59].
Overall, LPCD decreased as SWM implementation levels increased for all cities analyzed in this study. The implementation of SWM facilitated behavioral changes in water consumption, enhanced the quality of the information accessible to customers regarding their water usage, and encouraged water conservation practices.

3.2. Evaluation of DHW-Based Greenhouse Gas Emissions

In this section, the focus was on analyzing the energy consumption and carbon intensity of DHW in urban water systems, which was found to be the major consumer of energy in households. The GHG emissions resulting from DHW contribute 5.5% of the total annual GHG emissions, while other sections of the water treatment system only contribute 0.8% [60]. In this study, the estimation of DHW consumption was based on LPCD and the proportion of LPCD allocated to DHW in each city. GHG emissions were then estimated using Equations (1) and (2), without considering seasonal or environmental fluctuations. The findings, including the carbon intensity of DHW, LPCDDHW, and the estimated GHG emissions of DHW for five selected cities, are presented in Figure 3.

3.2.1. Southern Water Service Area, UK

Based on Figure 3a, Southern Water demonstrated an impressive near 50% reduction in GHG emissions linked to DHW consumption between 2011 and 2021 [61]. This accomplishment can be attributed to notable reductions in both LPCDDHW and the carbon intensity of DHW heating, decreasing from 463 gCO2e/L to 268 gCO2e/L. The reduction in carbon intensity was a result of the decarbonization of electricity and the increased utilization of renewable energy sources [35]. The interplay among the WEC nexus played a pivotal role in consistently reducing net GHG emissions over time, even when considering the almost equal levels of LPCDDHW in 2014 and 2020. This suggests that the decrease in the carbon intensity of water heating had a significant influence on GHG from DHW, despite the similarity in LPCDDHW levels between the two years.
Other researchers proposed that implementing SWM in England and Wales could potentially decrease emissions by 1.1–1.6 MtCO2e per year [21]. A similar study identified a missed opportunity resulting from the delayed implementation of SWM in residential areas and overlooking the WEC nexus. Incorporating this nexus could have potentially saved an average of 285,000 L of water and led to an annual reduction of approximately 2 million tons of CO2e in GHG emissions [62].

3.2.2. San Francisco, USA

San Francisco achieved a 96% SWM implementation level in 2014 [46], resulting in a gradual reduction in LPCDDHW from 58 L per day in 2011 to 50 L per day in 2021, coupled with a reduction in the carbon intensity of energy for water heating from 448 gCO2e/kWh in 2011 to 379 gCO2e/kWh in 2021 [35]. As a result of the mixed and synergistic effects of the WEC nexus components, the net GHG emissions of 1518 gCO2e/c/d in 2011 gradually reduced to 1032 gCO2e/c/d in 2021, as presented in Figure 3b. This case study highlights the impact of reducing both LPCDDHW and the carbon intensity of water heating on GHG emissions, similar to the Southern Water case in the UK.

3.2.3. City of Sacramento, USA

The GHG emissions of the City of Sacramento are presented in Figure 3c. After analyzing the SWM implementation data and the LPCDDHW trend for 7 years, LPCDDHW decreased from about 79 L in 2015 to about 65 L in 2021. An exception was noted in 2020, as the LPCDDHW increased to about 80 L mainly due to the COVID-19 outbreak [63]. As observed in previous cases, the reduction in GHG emissions highlights the significance of the WEC nexus. Despite minor decreases in LPCDDHW, a net reduction in GHG emissions was achieved. This outcome can be attributed to the continuous decline in the carbon intensity of water heating, dropping from 448 gCO2e/kWh in 2015 to 379 gCO2e/kWh in 2021 [35]. However, the increased LPCDDHW in 2020 caused a net increase in GHG emissions. Even modest reductions in LPCDDHW, when coupled with decreased carbon intensity, can significantly contribute to overall GHG emissions reductions.

3.2.4. Melbourne, Australia

From 2011 to 2021, Melbourne’s LPCDDHW fluctuated between 55 and 61 L, after drought restrictions were lifted in 2010 [64]. Melbourne has a low SWM implementation level of 5% [54], as displayed in Figure 2d, leading to an increase in the LPCDDHW associated with high water consumption, although the city installed water-saving appliances in 2010 [65]. Despite the minor increase in LPCDDHW in 2019, a net reduction in GHG emissions was achieved due to the substantial decarbonization of water heating, where the carbon intensity of water heating significantly decreased, leading to a net decrease in GHG emissions [66].

3.2.5. Sejong City, South Korea

According to Figure 3e, Sejong City witnessed a reduction in GHG emissions over time. The substantial decrease in GHG emissions from 4090 gCO2e/c/d in 2012 to 2080 gCO2e/c/d in 2020 in Sejong City can be attributed to the combined effects of reducing LPCDDHW and decreasing the carbon intensity of water heating [67]. Sejong City’s efforts to promote an eco-friendly environment included the installation of a carbon emissions monitoring system that tracks water, electricity, and gas consumption data as well as GHG emissions [68]. This system helped to maintain consistently lower GHG emissions in the city over time. Additionally, the carbon intensity of energy in South Korea decreased over the review period from 2012 to 2020 [35].

4. Comparative Discussion and Limitations

Increasing the level of SWM implementation generally leads to a decrease in LPCD, as presented in Figure 2. However, the impact of SWM on LPCD reduction can vary among countries due to the complex interactions with other water conservation measures. Overall, Figure 3 illustrates the substantial variations in water consumption and GHG emissions among the case studies with varying levels of SWM implementation. These findings emphasize the importance of developing a deeper understanding of the WEC nexus.
This study identifies two primary factors (i.e., the level of SWM implementation and the carbon intensity of water heating) that dominantly affect the WEC nexus. Although other factors (e.g., climate, demographics, gender, building type, end-use appliances, etc.) that could potentially impact LPCDDHW were not considered in this study due to the data limitations, the four hypothetical scenarios that were evaluated contribute to our understanding of the relationship among SWM implementation level, the decarbonization of water heating, and GHG emissions.
Scenario 1 serves as the baseline scenario, which does not consider the implementation of SWM or changes in the carbon intensity of water heating. It also disregards other factors that may influence LPCDDHW in households. The GHG emissions associated with DHW consumption in this scenario are used as a reference for comparison with the other scenarios. By comparing the relative GHG emissions of all other scenarios to that of scenario 1, the impact of the SWM implementation level and water heating decarbonization on GHG emissions is assessed. This evaluation of the relative GHG emissions across different scenarios helps identify the most effective measures for reducing GHG emissions.
In scenario 2, SWM implementation is the only change from the baseline scenario (scenario 1), and the carbon intensity of water heating remains the same as in scenario 1. Scenario 2 is used to observe the absolute impact of SWM implementation on GHG emissions without any influence from changes in the carbon intensity of water heating. From the results of scenario 2, an increased level of SWM implementation results in a considerable decrease in water consumption; thus, GHG emissions also substantially decline, as presented in Figure 4a,b,e. For instance, Southern Water in Figure 4a and San Francisco in Figure 4b, attaining high and early SWM implementation levels, recorded a notable decrease in GHG emissions due to a reduction in water consumption [41,46]. However, the magnitude of the GHG emissions reduction varied among the five case studies, emphasizing the complex interactions among water conservation interventions, SWM implementation levels, and other factors that impact LPCDDHW. For example, Melbourne exhibited almost similar relative GHG emissions as scenario 1, primarily due to the relatively small reduction in water consumption caused by delayed and low SWM implementation levels [54]. Conversely, Sejong City’s relative GHG emissions in scenario 2 significantly decreased, which is primarily attributed to the rapid reduction in LPCDDHW, possibly driven by increased water prices and the widespread usage of water-saving appliances [69].
The comparison revealed that scenario 3, which solely focused on the decarbonization of water heating without SWM implementation, resulted in greater reductions in GHG emissions compared to scenario 2. This highlights the potential of transitioning to fossil-free sources of electricity such as wind, solar, hydro, or nuclear power, in achieving significant GHG emissions reductions globally [70,71,72]. However, it is important to note that the extent of reduction varied among countries, as the carbon intensity of DHW production varies depending on the different global trends toward alternative and renewable energy use [73,74,75,76]. Despite this variation, all case studies in scenario 3 experienced reductions in GHG emissions, as depicted in Figure 4.
Scenario 4 is considered the most favorable option, as it combines the implementation of SWM and the reduction in carbon intensity in water heating, resulting in a synergistic effect that substantially reduces GHG emissions. A dual reduction in GHG emissions is achieved by reducing DHW consumption through SWM implementation and simultaneously lowering the carbon intensity of water heating. The combination of these interventions yields the most significant reduction in GHG emissions compared to all other scenarios, as illustrated in Figure 4.
Comparisons of the estimated GHG emissions for the presented scenarios should be interpreted with caution due to inherent assumptions and limitations in data availability. However, the exploration of these four scenarios offers valuable insights into the potential impact of SWM implementation and the decarbonization of water heating on GHG emissions. While specific information on the exact influence of the SWM implementation level and water heating decarbonization on GHG emissions is lacking, it can be inferred that implementing sustainable strategies, including SWM and the decarbonization of water heating, can positively contribute to reducing GHG emissions.
In summary, the analysis of the case studies and scenarios highlights the critical role of water conservation and energy decarbonization in addressing climate change. Scenario 2 demonstrates the positive impact of SWM implementation on reducing water consumption and GHG emissions. Scenario 3 emphasizes the significance of decarbonizing energy supplies for GHG emissions reduction. However, the most effective approach is seen in scenario 4, where the combination of SWM implementation and the decarbonization of water heating leads to the greatest GHG emissions reductions. These findings underscore the need for integrated strategies that consider both the water and energy sectors to successfully reduce GHG emissions [77]. It is essential to prioritize efforts to reduce GHG emissions from water consumption in both sectors, even in the absence of comprehensive policies and measures.

5. Limitations

This study focused on a limited number of case studies from four countries, potentially limiting the generalizability of the results. Data availability among the case studies varied, introducing uncertainties and biases. Hypothetical scenarios used in the analysis involved simplifications and assumptions that might not fully capture real-world complexities. The study’s timeframe covered a specific period, possibly overlooking long-term variations and seasonal influences. Comprehensive factors, such as climate, building types, and socio-economic aspects, were not considered due to data limitations. The observed correlations among SWM implementation, water consumption, and GHG emissions do not establish an absolute correlation, indicating the need for further investigation in future studies.
Despite the limitations, this study contributes valuable insights on the WEC nexus and the potential of SWM implementation to mitigate water consumption and GHG emissions. By analyzing case studies and presenting hypothetical scenarios, it sheds light on the complex interactions within urban water systems. Given the concerns about water scarcity and climate change, understanding strategies for water efficiency and GHG reduction is crucial. The findings have implications for policymakers, utilities, and researchers aiming to develop sustainable solutions. Recognizing the limitations helps pave the way for future research to deepen understanding and foster integrated strategies prioritizing water and energy conservation for a more sustainable future.

6. Conclusions

The implementation of SWM emerges as a highly effective tool for reducing water consumption and subsequently decreasing the GHG emissions associated with DHW consumption. The research findings revealed that cities with higher levels of SWM implementation exhibited substantial reductions in water consumption, leading to notable reductions in GHG emissions. Additionally, coupling SWM implementation with the decarbonization of water heating presented promising results for achieving significant GHG emissions reductions. This study demonstrated that the most effective approach was the combination of SWM implementation and the decarbonization of water heating, as revealed through hypothetical scenarios. Future studies will explore how DHW consumption is impacted by various factors, including the age of occupants, household size, the usage of water-using appliances, behavioral patterns, and the time of use. By considering these additional variables, we seek to gain a more comprehensive understanding of the complex interactions that affect DHW consumption and further enhance our insights into water usage patterns and potential water-saving opportunities.

7. Recommendations

Comprehensive assessments of SWM should be conducted to analyze its impact on both people and the environment. This entails a thorough exploration of the advantages and disadvantages for various groups. The integration of SWM with other water-conserving strategies, such as rainwater harvesting, greywater reuse, and energy-efficient appliances, should also be investigated. Moreover, implementing behavioral interventions, such as promoting social norms and providing personalized feedback, can play a crucial role in fostering water-efficient behaviors and enhancing the widespread adoption of SWM practices. Furthermore, it is essential to explore methods for incorporating renewable energy sources into water heating systems, which can lead to significant reductions in the GHG emissions associated with DHW. This integration aligns with efforts to mitigate climate change and minimize environmental impacts.

Author Contributions

Methodology, S.M. and H.W.G.; validation, J.M.L., S.K. and S.L.; investigation, S.M., H.W.G. and J.C.J.; resources, S.M. and J.C.J.; data curation, S.M., H.W.G., S.G.L. and J.C.J.; writing—original draft preparation, S.M. and J.C.J.; writing—review and editing, J.C.J., J.M.L., S.K. and S.L.; visualization, H.W.G., S.G.L. and S.M.; project administration, J.C.J. and J.M.L.; funding acquisition, J.M.L. and S.L. All authors have read and agreed to the published version of the manuscript.


This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00259994).

Data Availability Statement

The data are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Energy intensity of water treatment process and the average share (hot vs. cold water) of LPCD in households.
Figure 1. Energy intensity of water treatment process and the average share (hot vs. cold water) of LPCD in households.
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Figure 2. Impact of SWM implementation level on LPCD for selected cities.
Figure 2. Impact of SWM implementation level on LPCD for selected cities.
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Figure 3. Impacts of LPCDDHW and carbon intensities of electricity on GHG emissions.
Figure 3. Impacts of LPCDDHW and carbon intensities of electricity on GHG emissions.
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Figure 4. Comparative scenario evaluation for the impact of SWM implementation level and the decarbonization of water heating on GHG emissions in five case studies.
Figure 4. Comparative scenario evaluation for the impact of SWM implementation level and the decarbonization of water heating on GHG emissions in five case studies.
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Msamadya, S.; Joo, J.C.; Lee, J.M.; Lee, S.; Kim, S.; Go, H.W.; Lee, S.G. Estimated Impacts of Smart Water Meter Implementation on Domestic Hot Water Consumption and Related Greenhouse Gas Emissions from Case Studies. Water 2023, 15, 3045.

AMA Style

Msamadya S, Joo JC, Lee JM, Lee S, Kim S, Go HW, Lee SG. Estimated Impacts of Smart Water Meter Implementation on Domestic Hot Water Consumption and Related Greenhouse Gas Emissions from Case Studies. Water. 2023; 15(17):3045.

Chicago/Turabian Style

Msamadya, Spancer, Jin Chul Joo, Jung Min Lee, Sangho Lee, Sangrae Kim, Hyeon Woo Go, and Seul Gi Lee. 2023. "Estimated Impacts of Smart Water Meter Implementation on Domestic Hot Water Consumption and Related Greenhouse Gas Emissions from Case Studies" Water 15, no. 17: 3045.

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