The Responsiveness of Urban Water Demand to Working from Home Intensity

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

General comments:

Thank you for this interesting paper. It is certainly a timely topic. I have a couple of recommendations and suggestions before I can recommend it for publication.

Several citations or references to tables and figures were not displayed correctly. Please check the citation program. It was therefore hardly possible to check whether the text was correctly matched to the referenced sections.

ABSTRACT
It would be nice to read the key result in the abstract.

INTRODUCTION
In the last couple of years impacts on water consumption due to behaviour changes measured during COVID was prominent. Despite a short overview on some studies, I feel there is a lot missing. 

METHODOLOGY
In general, I would recommend using the number of consecutive days without rain (length of dry periods) as a proxy for water consumption rather than the number of days with rainfall. It has been shown in other areas, that water consumption increases the longer the dry period.

I am also missing data or a proxy for industrial and business activities. Industrial and business activities would likely influence the overall water consumption quite drastically if production was reduced at any point in time. It would be good to consider monthly GDP or import/export data as a proxy for industrial and business activities.

As I am not familiar with Sydney Water sector system, the codes that are shown in brackets are confusing. A distribution map might be helpful and a clarification needed.

REFERENCES
References should all have the same format. Some are in capital letters some are not. Please keep them uniform.

Specific comments:

Line 26: I assume "May 2023" should be "May 2021". Otherwise, please clarify.

Line 28, 440, 473, 476, 477, 481, 538: It looks like there is one blank space too many. 

Line 73-74: Please revise this sentence. Is there something missing? What did they find?

Line 434-436: Please revise this sentence. It is hard to understand.

Line 440: Is there a blank space too much? 

Line 490: [...], would result in increased water consumption [...]

Author Response

Comment: Thank you for this interesting paper. It is certainly a timely topic. I have a couple of recommendations and suggestions before I can recommend it for publication.

Response: Thanks you.

Comment: Several citations or references to tables and figures were not displayed correctly. Please check the citation program. It was therefore hardly possible to check whether the text was correctly matched to the referenced sections.

Response: We have changed all of these. Strangely the references to tables and figures shows up correctly in our version, which meant we didn’t realise it wasn’t showing correctly for the reviewers’ version.

ABSTRACT
Comment: It would be nice to read the key result in the abstract.

Response: We have now added such key results into the abstract, but as a result have had to make some significant changes to the abstract in order to stay within 200 words.

INTRODUCTION

Comment: In the last couple of years, impacts on water consumption due to behaviour changes measured during COVID was prominent. Despite a short overview on some studies, I feel there is a lot missing. 

Response: I have redone a search of papers looking specifically on the impact of working from home on water use, and frankly there isn’t many new papers on the topic. However, the reviewer is right that there is now more literature on the impact of COVID on water use so we have now included a new paragraph on this.

A number of studies have explored the impact of COVID-19 on urban water use. Vi-zanko et al [25] found, using an Agent-based simulation model that social distancing in itself leads to an increase in residential water demand. Shu et al [26] showed that in the context of China, commercial water use was significantly reduced as a result of COVID-19 related indicators such as reduced mobility. Sabzchi-Dehkharghani et al [27], analysing water demand data from Tabriz, Iran, found that residential water demand increased during the pandemic, but decreased again after the pandemic, with socio-economic fac-tors and population density having a considerable impact on the size of demand changes. Ribas et al [28], analysing residential water use data from during the most intense parts of the COVID-19 pandemic, in Northeast Spain, found that indoor water uses increased sig-nificantly during the pandemic, and argued that this was linked to greater prevalence of WFH behaviours. Hackbarth et al [29] analysed water use data in commercial buildings in southern Brazil, and found that certain types of businesses, like law and accounting firms saw a 28% reduction in water using during COVID-19, whilst engineering offices had an increase in water use of about 15%, showing the influence of the type of economic activity on commercial water demand. Almulhim and Aina [30] undertook a household survey in Saudi Arabia and found that 50-86% of respondents self-reported increases in water bills, which impacted on their household budgets. As an interesting data point, Gholami et al [31] reported an overall increase in water use of 10-15% in different cities across Iran dur-ing the COVID-19 pandemic. Buurman et al [32] reviewed the overall evidence of water use changes during COVID-19 and identified changes in daily water demand patterns, and that residential water use increased between 6% and 13%, and with some changes in commercial and industrial sectors unclear (although indications are it reduced).

METHODOLOGY

Comment: In general, I would recommend using the number of consecutive days without rain (length of dry periods) as a proxy for water consumption rather than the number of days with rainfall. It has been shown in other areas, that water consumption increases the longer the dry period.

Response: We tried both, and for the purposes of this study, it didn’t make a difference. So we’re sticking with average monthly days of rainfall.

Comment: I am also missing data or a proxy for industrial and business activities. Industrial and business activities would likely influence the overall water consumption quite drastically if production was reduced at any point in time. It would be good to consider monthly GDP or import/export data as a proxy for industrial and business activities.

Response: This is a valid point. At the aggregate level (all sectors combined) there should be a long-run relationship between water consumption and GDP. In Australia GDP expansion, to a large extent, reflects population growth. As population and overall GDP grow so does water consumption as more properties house the increased population. Overall, residential water consumption constitutes 75% of Sydney’s aggregate water consumption. The relationship between GDP and water consumption is, however, orthogonal to the remaining variables in the regression (temperature, rainfall, working from home, and water restriction policies). When adding monthly GDP to the aggregate water consumption regression, the relationship is significant and with an elasticity of approximately 1 (0.968, p=0.022). Remaining variables are only affected in a minor way and without substantive impact on interpretation. The aggregate regression has been updated to include GDP index.

When adding monthly GDP to the sectoral regressions. There is no longer any effect (and remaining coefficients are not affected). The sectoral regressions are based on consumption per property (house, apartment, business unit etc). On a per property basis there is not the same expectation of a long-run relationship. For instance, a doubling of GDP reflects a very large increase in population and thus water consumption by households and businesses (more businesses too). On a per property basis a doubling of GDP is neither associated with an increase in household sizes (which would increase the per property consumption) or a proportional increase in economic output per business (keeping in mind that most businesses are small and medium enterprise, their numbers also increasing as the economy expands). The sectoral regressions have not been updated to include GDP index.

We have made the following edit to the text to draw readers’ attention to these points in section 3.3: “Measures of economic activity, such as GDP, are not included in Eq 1. In the empirical analysis the monthly GDP index is added to the aggregate regression. GDP expansion in Australia significantly reflects population growth. As population and economy grow aggregate water demand increases. The sectoral regressions are based on water consumption per property. As population and economy expand so does the number of properties in the economy. In both cases, GDP expansion is, for the examined time period, orthogonal to remaining variables in Eq 1.”  

Comment: As I am not familiar with the Sydney Water sector system, the codes that are shown in brackets are confusing.

Response: Further explanation is provided in the appendix, which I now realised wasn’t submitted here. I will include it in the next version.

Comment: A distribution map might be helpful and a clarification needed.

Response: We are unsure what precisely is meant here. The distribution of water consumption by sector has been added to section 3.1.1 Water Demand Data.

REFERENCES
Comment: References should all have the same format. Some are in capital letters some are not. Please keep them uniform.

Response: This is a function of EndNote formatting, which I noticed was using the wrong style for Sustainability. This has now been updated. There were also some references imported which for some reasons were capitalised. Since updating this, I can’t find any inconsistency in capitalising.

Specific comments:

Comment: Line 26: I assume "May 2023" should be "May 2021". Otherwise, please clarify.

Response: Updated.

Comment: Line 28, 440, 473, 476, 477, 481, 538: It looks like there is one blank space too many. 

Response: Updated.

Comment: Line 73-74: Please revise this sentence. Is there something missing? What did they find?

Response: Yes, the sentences were broken. Fixed now. Thanks.

Comment: Line 434-436: Please revise this sentence. It is hard to understand.

Response: It now reads “Previous reported that WFH unsurprisingly led to an increase in water demand for residential properties, whilst our analysis indicate that the increases in residential water demand is primarily for apartments and units, but surprisingly no statistically significant impact found for residential water demand in single-home dwellings.” Hopefully this is better.

Comment: Line 440: Is there a blank space too much? 

Response: as per above, this has been sorted.

Comment: Line 490: [...], would result in increased water consumption [...]

Response: We have updated the sentence to now read “Permanent changes to average temperatures, for instance because of climate change and exacerbated by urban patterns that create heat islands (i.e., due to poor urban designs), leads to in increased water demand across all sectors.”

Reviewer 2 Report

Comments and Suggestions for Authors

The article under review presents an analysis of the impact of remote work (WFH - Work From Home) on water usage in an urban context, focusing on sectoral data regarding water consumption in Sydney, Australia. The central question is: How will a percentage change in the prevalence of remote work practices affect sectoral water demand? By employing an Auto-Regressive Distributed Lag (ARDL) model, the authors scrutinize the variability in WFH’s influence on water demand, taking into account disruptive factors such as temperature, precipitation, water consumption trends, water supply restrictions, and others. The article highlights sectoral water consumption in relation to WFH, pointing out that overall water consumption patterns might obscure sectoral shifts pertaining to where and when water is used. The study's novelty is underscored by its use of mobility data as a proxy for remote work intensity, fostering a more nuanced understanding of remote work's impact, distinctly from the effects of lockdowns. The article addresses a research gap concerning the influence of remote work (WFH) on sectoral water demand, particularly focusing on the elasticity of this demand in response to fluctuations in remote work intensity. Other ongoing research suggests that household water demand escalates with remote work, while demand in other sectors likely decreases. This study contributes finer-grained data on this subject and employs innovative methods like the ARDL model for analyzing dependencies. The conclusions of the paper are in harmony with the presented evidence and reasoning. The study revealed that an increase in the intensity of remote work did not result in a statistically significant impact on the overall water demand in Sydney. However, it was observed that residential blocks might experience a surge in water demand, whereas a significant decrease in water demand was noted in the commercial, mixed, and industrial sectors. I have no objections to the methodology used in the article. The references are appropriate. I have three remarks:
1.    The aim of the article should be clearly articulated in both the introduction and the summary.
2.    Consideration of additional analyses concerning the long-term effects of shifts in water consumption and their potential impact on sustainable development strategies would be beneficial.
3.    It is advisable for the authors to include a section elucidating the limitations of the study.

Author Response

The article under review presents an analysis of the impact of remote work (WFH - Work From Home) on water usage in an urban context, focusing on sectoral data regarding water consumption in Sydney, Australia.

 The central question is: How will a percentage change in the prevalence of remote work practices affect sectoral water demand? By employing an Auto-Regressive Distributed Lag (ARDL) model, the authors scrutinize the variability in WFH’s influence on water demand, taking into account disruptive factors such as temperature, precipitation, water consumption trends, water supply restrictions, and others. The article highlights sectoral water consumption in relation to WFH, pointing out that overall water consumption patterns might obscure sectoral shifts pertaining to where and when water is used. The study's novelty is underscored by its use of mobility data as a proxy for remote work intensity, fostering a more nuanced understanding of remote work's impact, distinctly from the effects of lockdowns. The article addresses a research gap concerning the influence of remote work (WFH) on sectoral water demand, particularly focusing on the elasticity of this demand in response to fluctuations in remote work intensity. Other ongoing research suggests that household water demand escalates with remote work, while demand in other sectors likely decreases. This study contributes finer-grained data on this subject and employs innovative methods like the ARDL model for analyzing dependencies. The conclusions of the paper are in harmony with the presented evidence and reasoning. The study revealed that an increase in the intensity of remote work did not result in a statistically significant impact on the overall water demand in Sydney. However, it was observed that residential blocks might experience a surge in water demand, whereas a significant decrease in water demand was noted in the commercial, mixed, and industrial sectors. I have no objections to the methodology used in the article. The references are appropriate.

Response: Thanks for this summary which I think are as we see it as well. I am glad you found it a useful paper.

Comment: The aim of the article should be clearly articulated in both the introduction and the summary.

Response: In the introduction, we already state this in the research questions addressed. However, we have now also added a first introductory sentence to the paper, as well as in the Conclusions.   

Comment: Consideration of additional analyses concerning the long-term effects of shifts in water consumption and their potential impact on sustainable development strategies would be beneficial.

Response: We have added further section on what the results mean, as well as suggestions for further research.

 

Comment: It is advisable for the authors to include a section elucidating the limitations of the study.

Response: We have added a section on limitations and suggestions for further study.

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,
The article discusses a significant problem with the management of drinking water resources during the pandemic in the city of Sydney. Nevertheless, the authors did not avoid making several mistakes. They result mainly from the lack of markings for tables, and figures in the text, as well as supplements to the literature. In my opinion, the article only answers that WFH does not make a significant difference to water supply problems. This situation should be expected because water installations are always designed with a surplus. I included the remaining comments in the text. I believe that the article should be corrected before it is published.

Comments for author File: Comments.pdf

Author Response

Comment: In my opinion, the article only answers that WFH does not make a significant difference to water supply problems. 

Response: This is not true. The water use changes within each of the sectors and this is important for water planning. In other locations the aggregate demand has changed (sometimes increased, sometimes decreased), and in fact our study shows why this inconsistency may be the case. It depends on the context and nature of the city. The type of urbanisation, and commercial/industrial mix etc.

Abstract

3.1. Comment: Temperature is not a confounding factor.

Response. We agree with this. It is rather a factor with direct influence on water demand, which varies per sector. Therefore we account for it, but you are right it’s not a confounding factor (although it could affect WFH behaviours as well), and have removed the term confounding for “temperature”.

3.2. Comment: What do you mean by trends?

Response: Given we are using a Time Series Model, we refer to linear changes in water use over time.

3.3. Comment: “with respect” is not necessary.

Response: Sure, we have removed it.

 

Introduction

3.4. Comment: “Remove comma”

Response: Removed

3.5. Comment: Change “Gap in knowledge” with “Knowledge gap”.

Response: Don.

3.6. Comment: Spell out full abbreviation for RQ

Response: Write Research Question.

 

Main text

3.7. Comment: Fix broken links to figures and tables.

Response: Interesting that these were broken because in our version they are not. We have spelled out without using MS Word cross-referencing.

3.8. Comment: Spell out Per Property in tables.

Response: Done.

3.9 Comment: What does the analysis of such independent variables provide? [Note: rainfall and temperature]

Response: We have added a sentence “Including these variables allow us to separate out effects on water demand that relate to variations in weather conditions, and this then allows us to more clearly see the impact of WFH patterns.”

3.10. Comment: Add units to Figure 2 Vertical Axis.

Response: Done.

3.11. Comment: Add units to Figure 3 Vertical Axis.

Response: Done.

3.12. Comment: Why are these two completely separate parameters treated interchangeably?

Response: They are not, except in terms of it’s representation in the above equation (or at least in the previous version of it), for simplicity illustrating we include both as weather related variables. However, fn fact the equation has been updated and doesn’t include this short hand any more, so we need to update the equation. So it’s simply an error that this is so, which we have corrected.

3.13. Comment: Add units to tables.

Response: Done.

3.14. Comment: Section 3.1.3 is unnecessary.

Response: We disagree because the dates of changes are providing context, and shows all the various times we test for – most of which have no impact.

3.15. Comment: Move part of the text to the methodology.

Response: Ok, done.

Reviewer 4 Report

Comments and Suggestions for Authors

The main drawbacks of the article are the following:

The objectives of the study need more discussion with literature to be supported.

The tables are so many and are not critically analyzed, which can provide an unclear path for future researchers to replicate the study.

The results are not critically analyzed. The results must be interpretive rather than just descriptive, and the research results must be connected with relevant literature citations for validity and reliability.  And also the discussion should be improved by a dialog with literature.

 

Discussing the results could be improved by interpreting them to support theories related to the research topic.

 

 

The research data does not support the conclusions, which does not indicate a more straightforward path for future studies on the topic.

 

Finally, we suggest improving the logical flow of the paper and underlining the relationship between the paper's objectives and the aim of the Sustainability journal.

 

Good luck!

Comments on the Quality of English Language

Some minor editing of the English language is required.

Author Response

Comment: The objectives of the study need more discussion with literature to be supported.

Response: This is present in the Introduction, section 1.2 and 1.3, which have now also been extended even further to clarify this.

 

Comment: The tables are so many and are not critically analyzed, which can provide an unclear path for future researchers to replicate the study.

Response: We are unsure how to respond to this. Table 1 and 2 provide descriptive contextual data on timing of Covid related events, and timing of water restriction events. These tables, rather than providing the basis for critical discussion, are included to give readers an understanding of contextual and chronological factors relating to the analysis period and structure of the later econometric framework. 

Table 3 contains summary statistics for the ADF tests. Interpretation and reflctions are presented in 5 points. Table 4 tests for structural breaks in the water consumption series. Interpretation and reflctions are presented in 3 bullet points. Overall implications of Table 3 and 4 for the modelling framework are also discussed. ADF tests are typical of timeseries analysis and used to inform the structure of subseqnet econometric modelling. Table 5 provides the estimation results and is interpreted in Section 4.2 and criticall discussed throughout Section 5.

Comment: The results are not critically analyzed. The results must be interpretive rather than just descriptive, and the research results must be connected with relevant literature citations for validity and reliability. And also the discussion should be improved by a dialog with literature. Discussing the results could be improved by interpreting them to support theories related to the research topic.

Response: We have added much more discussion about the results (I note that separation of results and discussion is appropriate in scientific papers) and have provided many more references to this.

 

Comment: The research data does not support the conclusions, which does not indicate a more straightforward path for future studies on the topic.

 

Response: I am not sure what is meant by this, but we have added sections on proposed future research, and limitations.

 

Comment: Finally, we suggest improving the logical flow of the paper and underlining the relationship between the paper's objectives and the aim of the Sustainability journal.

 

Response: We have made many changes to the paper which hopefully will improve the logic flow, as well as stressed the link with sustainability throughout.

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks for improving the manuscript. 

Unfortunately, I needed some time to realise the crossed-out passages were the ones that were in fact newly inserted and the underlined ones were the ones to be removed. It seems that you compared two versions and used the wrong file as original version. But as you mentioned before it might also be an issue with the uploading system. 

Author Response

Thank you. Nothing further to respond to.

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

Your article was much improved. However, I found some mistakes in the marks in Table 3. What is "s" used in almost every unit? Rainfall is in mm. What ML and kL is? Is it 1000m3 and 1 m3? These units are more self-explanatory and common. I suggest you to change them.
Besides, in my opinion, the article is ready to be published.

Author Response

Thanks for letting us know about the additional 's' in the units in Table 3. We have edited this now.

Reviewer 4 Report

Comments and Suggestions for Authors

Good luck!

Author Response

Thank you. Nothing further to respond to.