Does Government Environmental Expenditure Reduce Residential Energy Consumption in Canada? Evidence from Provincial Panel Data

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

"Government spending ranges from 25% to 59% of Gross Domestic Product (GDP) across OECD countries in 2021" for environmental protestion, I strongly doubt, please explain

in 2.1, you give sources relevant to the topic, however not the results of the respective studies, please update

avoid quotation lumps (like 4, 16 - 18) one brief quotation from each reference

please number formulae and explain the step from formula 1 to 2

The discussion is fine, but can you present the government investment per ton CO2 avoided and discuss the reduction of CO2 in this light

"high inelasticity" and "the inelastic nature of price elasticity for residential energy" please explain; does this indicate that energy saving is not connected to level of energy price? this does not sound convincing

Where do the explanations regarding elderly peoples´energy consumption come from, please give source. The observation that higher income means more energy consumption and heating degree days as well, is not exactly new. can you establish a ranking in between the factors, like what is the strongest impact, next one, etc.

heating degree days play a pivotal role, but relate to annual weather changes, they have nothing to do with government policy, please explain.

 

 

 

 

 

Author Response

1. Summary
Thank you very much for taking the time to review this manuscript and for providing insightful comments and suggestions. Please find below the detailed responses, along with the corresponding revisions and corrections highlighted in red in the re-submitted files.

Response to Reviewer 1 Comments

Open Review

( ) I would not like to sign my review report
(x) I would like to sign my review report

Quality of English Language

( ) The English could be improved to more clearly express the research.
(x) The English is fine and does not require any improvement.

 

2. Questions for General Evaluations	Yes	Can be improved	Must be improved	Not applicable
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	(x)	( )	( )	( )
Are the research design, questions, hypotheses and methods clearly stated?	(x)	( )	( )	( )
Are the arguments and discussion of findings coherent, balanced and compelling?	(x)	( )	( )	( )
For empirical research, are the results clearly presented?	(x)	( )	( )	( )
Is the article adequately referenced?	(x)	( )	( )	( )
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	(x)	( )	( )	( )

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: "Government spending ranges from 25% to 59% of Gross Domestic Product (GDP) across OECD countries in 2021" for environmental protestion, I strongly doubt, please explain.

Response 1: The manuscript states that “total government spending ranges from 25% to 59% of Gross Domestic Product (GDP) across OECD countries in 2021 [5].” These percentages reflect overall government spending, not specifically spending on environmental protection (lines 24-26 on page 2)

Comments 2: in 2.1, you give sources relevant to the topic, however not the results of the respective studies, please update.

Response 2: Section 2.1 has been re-written to provide the results of the respective studies as shown below (see lines 10-35 on page 4):

Research on the influence of environmental policies on residential energy consumption has remained relatively limited. Several studies—including those by Tsemekidi et al. [8], Bertoldi and Hirl [9], Bertoldi and Mosconi [10], and Horowitz and Bertoldi [11]—have used panel data and diverse indicators to evaluate the impact of EU policies on energy conservation in the residential sector. Employing an econometric energy demand model, Filippini and Hunt [12] examined the effects of US residential energy efficiency policies, while Filippini, Hunt, and Zoric [13] applied a stochastic frontier approach to assess the effectiveness of EU energy efficiency measures in households. Their findings highlight that financial incentives and energy performance standards play crucial roles in driving energy efficiency investments, whereas information-based measures tend to have more limited impacts.

Tsemekidi et al. [8] also investigated the effectiveness of EU energy efficiency policies—including eco-design, energy labeling, building codes, and directives—in reducing residential energy consumption. Collectively, these studies underscore the significant role of energy efficiency measures, particularly Energy Efficiency Obligation Schemes (EEOS), in achieving substantial energy savings and advancing the EU’s climate objectives. Bertoldi and Hirl [9] and Bertoldi and Mosconi [10] highlight the importance of policy flexibility, stability, and robust monitoring for the successful implementation of EEOS. Tsemekidi Tzeiranaki et al. [8] quantify the EU’s energy savings potential, demonstrating that current policies can meet the 2030 climate targets. Horowitz and Bertoldi [11] reinforce the economic case, showing that energy efficiency investments are cost-effective compared to supply-side solutions.

Further studies have explored the impact of specific policies on residential energy use. For example, Aydin and Brounen [14] assessed the influence of mandatory energy efficiency labels for household appliances and building standards, concluding that both measures contributed to lower residential energy consumption. Broin et al. [15] analyzed EU space heating energy efficiency policies by categorizing them into financial, regulatory, and informative measures, highlighting that regulatory measures were particularly effective.

Comments 3: avoid quotation lumps (like 4, 16 - 18) one brief quotation from each reference.

Response 3: Lump quotations were avoided. Instead, one brief quotation from each reference was used, as shown below (see lines 37–38 on page 4 and lines 1-6 on page 5).

Government intervention to combat environmental pollution typically includes imposing taxes on energy use, establishing property rights to facilitate negotiations, and increasing government spending to encourage environmentally friendly behavior [4]. Studies highlight that government spending improves environmental quality in Northern African countries but has a negative effect in Southern African countries [16]. In China, environmental investments in one province positively influence neighboring provinces, demonstrating a spillover effect [17]. Moreover, the composition of government spending plays a crucial role, with environmental-focused spending leading to better environmental outcomes [18].

Comments 4: please number formulae and explain the step from formula 1 to 2

Response 4: Formula were numbered and the step from formulae 1 to 2 was explained as shown below (see lines 1-3 on page 9).

 pcrenu_it = f(sgexp_it, pelect_it, pcgdp_it, spop65_it, hdd_it, cdd_it, pd_it)                                       (1)

With log transformation on both sides, the above model without subscript can be written as

   lpcreu = ß₀ +ß₁pd +ß₂sgexp + ß₃pelect +ß₄lpcgdp +ß₅spop65 +ß₆lhdd +ß₇lcdd +ε      (2)

Note that sgexp, pelect and spop65 are expressed as percentages and, therefore, do not require a logarithmic transformation. Additionally, pd is a discrete variable taking the value 0 or 1 and does not need to be logged.

Comments 5: The discussion is fine, but can you present the government investment per ton CO2 avoided and discuss the reduction of CO2 in this light.

Response 5: In the discussion there is no statement on “the government investment per ton CO2”

Comments 6: "high inelasticity" and "the inelastic nature of price elasticity for residential energy" please explain; does this indicate that energy saving is not connected to level of energy price? this does not sound convincing.

Response 6: Some rationales explaining the inelastic nature of price elasticity for residential energy are provided as shown below (see lines 31-34 on page 15 and lines 1-2 on page 16).

Household energy is considered a basic necessity in modern households, required for essential activities like heating, cooling, cooking, and lighting. As a result, even when energy prices change, households find it challenging to significantly reduce their consumption in the short term because of the limited availability of substitutes and the essential role energy plays in daily life. This does not indicate that energy saving is not connected to the level of energy price. In the long run, it may be relatively more elastic.

Comments 7: Where do the explanations regarding elderly peoples´energy consumption come from, please give source. The observation that higher income means more energy consumption and heating degree days as well, is not exactly new.

Response 7: Some explanations regarding elderly peoples’ energy consumption with some references were given as shown below (see lines 20-30 on page 16 in the text).

The composition of the population, particularly the share of elderly individuals, significantly affects household energy use. While older individuals typically spend more time at home and use more appliances—leading to increased energy consumption—factors like budget constraints and environmental awareness can mitigate these effects. Our estimates reveal that a one-percentage-point increase in the elderly share results in a slight decline (-0.06)% in per capita energy use, contrasting with some studies that report a positive relationship. Estiri and Zagheni (2019) attribute rising energy use to housing size, while Inoue et al. (2021) found that Japan’s aging population raised household energy consumption by 12% from 1995 to 2015. A study in Innovation in Aging similarly suggests that aging influences energy use via changes in household size and consumption. These findings stress the importance of demographic trends in shaping energy policy.

Comments 8: can you establish a ranking in between the factors, like what is the strongest impact, next one, etc.

Response 8: A ranking of the factors in terms of their impact was made as shown below (see lines 1-4 on page 16 of the text)

Taking all factors into account, our results show that heating degree-days have the strongest influence on per capita residential energy use, followed by government environmental expenditure. In contrast, energy price has the weakest influence on per capita residential energy use in Canada.

Comments 9: heating degree days play a pivotal role, but relate to annual weather changes, they have nothing to do with government policy, please explain.

 Response 9: Yes, I do agree that both heating and cooling degree days are unrelated to government policy but may significantly influence household energy consumption. But these variables are included in the model as control variables.

4. Response to Comments on the Quality of English Language

Point 1: "The reviewer noted that the English is satisfactory and does not require any improvement."

5. Additional clarifications

There are no additional clarifications, as I have responded to and addressed each of the reviewers' comments.

Reviewer 2 Report

Comments and Suggestions for Authors

The article examines a period of 25 years. It is not advisable to take such a long period, as energy consumption is highly variable. This is due to the increase in electricity tariffs, replacement of equipment with energy-saving equipment, and a number of other factors.

The article takes into account factors such as weather, income, demographic shifts and energy prices, which affect household energy consumption trends. It is not clear how these factors can be combined. Especially weather or climate variables.

Demographic shifts. If we talk about population growth and take into account that the appliances used 25 years ago are completely different in terms of energy consumption, how relevant is this factor?

Panel data analysis is a powerful tool, but it has its limitations. For example, possible problems with endogeneity and multicollinearity can affect the accuracy of the estimates.

Which sector is being considered - housing or manufacturing?

What are the government's programmes aimed at improving the environment? It is not clear from the article.

More research is needed to better understand the effectiveness of public spending in different regions of Canada, taking into account the specifics of local conditions and policies.

In developing countries, residential energy consumption is shaped by a variety of factors, including GDP per capita, population, and electrification rates (p. 5). Why compare Canada to such countries?

Table 1 Name, specification and source of each variable. It would be advisable to provide numerical values instead of references.

The factors that shape household energy consumption can be divided into two categories: internal and external determinants. Where did this division come from, on what grounds, and why?  An explanation is needed.

Figure 1 shows a negative trend illustrating the inverse relationship between pcreu and sgexp. How does it illustrate this? You need a description that shows this.

The test includes the methods of Levin, Lin, Chu, Im, Pesaran, and Sheehan, as well as the extended Dickey-Fuller method. Which method was used in the article and why is it better?

Long-term and short-term evaluation. Why was it conducted? What did it reveal in relation to the topic of the article?

The title of the article needs to be clarified. The title should be consistent with the research.

The percentage of text uniqueness should be increased.

The layout of the article does not meet the requirements.

 

Comments on the Quality of English Language

The English language needs to be improved.

Author Response

Response to Reviewer 2 Comments                                                                                                                       

1. Summary 

We would like to thank the reviewer for their helpful comments and suggestions. We have addressed each comment individually and incorporated the necessary revisions into the manuscript where appropriate. Please find my detailed responses below, with the corresponding changes clearly highlighted in red in the revised submission.

Open Review

( ) I would not like to sign my review report
(x) I would like to sign my review report

Quality of English Language

(x) The English could be improved to more clearly express the research.
( ) The English is fine and does not require any improvement.

2. Questions for General Evaluations

2. Questions for General Evaluations	Yes	Can be improved		Must be improved		Not applicable
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	( )		( )		(x)		( )
Are the research design, questions, hypotheses and methods clearly stated?	( )		( )		(x)		( )
Are the arguments and discussion of findings coherent, balanced and compelling?	( )		( )		(x)		( )
For empirical research, are the results clearly presented?	( )		( )		(x)		( )
Is the article adequately referenced?	(x)		( )		( )		( )
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	( )		( )		(x)		( )

3. Point-by-point response to comments and suggestions for Author

Comments 1: The article examines a period of 25 years. It is not advisable to take such a long period, as energy consumption is highly variable. This is due to the increase in electricity tariffs, replacement of equipment with energy-saving equipment, and a number of other factors.

Response 1: I respectfully disagree with this view for the following reasons.

Using long periods of data to analyze factors affecting residential energy consumption is vital because it captures long-term trends and structural changes like change in technology, building code updates, and demographic shifts, which short-term data may miss. It also smooths out temporary fluctuations such as weather changes or energy price volatility, offering clearer insights. Moreover, longer datasets provide more observations, strengthening the reliability of econometric models and reducing the risk of misleading findings. Importantly, long-term data is crucial for evaluating the effectiveness of energy-related policies, which often require years to demonstrate their full impacts on household energy use. Collectively, these benefits make long-term data essential for informed energy policy and planning, which is done in this study.

Comments 2: The article takes into account factors such as weather, income, demographic shifts and energy prices, which affect household energy consumption trends. It is not clear how these factors can be combined. Especially weather or climate variables.

Response 2: Some explanations are given below see equation 2 on page 8 for detail.

The variables affecting household energy consumption are not combined; instead, each factor is included as a separate variable in the model to estimate their partial effects while holding other variables constant. This approach is consistent with standard econometric practices. To capture the impact of weather, we include Heating Degree Days (HDD) and Cooling Degree Days (CDD).

Comments 3: Demographic shifts. If we talk about population growth and take into account that the appliances used 25 years ago are completely different in terms of energy consumption, how relevant is this factor?

Response 3: Demographic shifts—including population growth, household size and composition, and aging—are key drivers of household energy demand. These trends also interact with technological changes, such as improvements in appliances and building energy performance, which can significantly affect residential energy use. Although demographic shifts are typically gradual, they have long-term effects on energy demand and are crucial for accurate forecasting and effective energy policy design. It is worth noting that the variable for demographic shifts is included as a control variable.

Comments 4: Panel data analysis is a powerful tool, but it has its limitations. For example, possible problems with endogeneity and multicollinearity can affect the accuracy of the estimates.

Response 4: Some explanations are given below (see Table 3 on page 11)

Panel data analysis provides key advantages despite potential issues like endogeneity and multicollinearity. It effectively captures unobserved heterogeneity by controlling for individual-specific effects that remain constant over time, such as household preferences or regional characteristics, which helps reduce bias. Moreover, panel data tracks dynamic changes by capturing variations across both time and individuals, enabling researchers to explore how factors like energy consumption or policy effects evolve over time. With multiple observations per unit, panel data also improves estimation efficiency, boosting the statistical power and precision of the analysis. Despite challenges, it remains a valuable tool for energy and environmental policy research. Notably, the degree of multicollinearity among explanatory variables in this study remains within acceptable limits (see Table 3 on page 11).

There could be an endogeneity issue. However, government environmental expenditure is typically driven by broader environmental goals and policy mandates rather than as a direct response to changes in household energy consumption. Therefore, the possibility that government environmental expenditure suffers from reverse causality in its effect on household energy consumption is low because governments allocate spending for environmental protection—such as subsidies for energy-efficient housing, renewable energy incentives, and public awareness campaigns—independently of fluctuations in household energy consumption.

Comments 5: Which sector is being considered - housing or manufacturing?

Response 5: The study focuses on the housing sector, as indicated by the title of the paper. Please refer to the title of the manuscript for clarification.

Comments 6: What are the government's programmes aimed at improving the environment? It is not clear from the article.

Response 6: Thanks for the comment. Various programs and policies to enhance environmental protection in Canada have been provided as shown below (see lines 3–9 on page 3).

Canada has launched various programs and policies to enhance environmental protection. Major initiatives include the Low Carbon Economy Fund, which funds energy efficiency projects; the Nature Smart Climate Solutions Fund, a $1.4 billion, ten-year program for ecosystem restoration; and the EcoAction Community Funding Program, which supports local environmental projects. Other programs include the Environmental Damages Fund, the Canada Nature Fund, the Canada Greener Homes Initiative, and the Canada Water Agency, all reflecting Canada’s strong environmental commitment.

Comments 7: More research is needed to better understand the effectiveness of public spending in different regions of Canada, taking into account the specifics of local conditions and policies.

Response 7: I fully agree with the comment that further research is needed to assess how the effectiveness of public spending varies across regions in Canada, given differences in local conditions and policies. However, this study cannot address these variations due to data limitations.

Comments 8: In developing countries, residential energy consumption is shaped by a variety of factors, including GDP per capita, population, and electrification rates (p. 5). Why compare Canada to such countries?

Response 8: The literature review focuses on identifying the drivers of residential energy consumption in both developed and developing countries, rather than making direct comparisons between Canada and these countries.

Comments 9: Table 1 Name, specification and source of each variable. It would be advisable to provide numerical values instead of references.

Response 9: Table 1 specifies each variable and its data source. Providing numerical values for each variable is not feasible, as the dataset includes ten panels with 25 years of observations each.

Comments 10: The factors that shape household energy consumption can be divided into two categories: internal and external determinants. Where did this division come from, on what grounds, and why?  An explanation is needed.

Response 10: Thanks for the comment. Some explanations are given as shown below.

 The division between internal and external determinants of household energy consumption depends on the source of the influencing factors: internal determinants arise from household-specific characteristics, while external determinants come from the broader environment and policy context. Segmenting these factors helps researchers and policymakers identify which aspects are within household control and which require external policy interventions. This approach supports the design of targeted programs—like subsidies for retrofits that address external price signals and awareness campaigns that influence internal behaviors. It also clarifies how external factors, such as energy prices, interact with internal behaviors like thermostat settings.

Comments 11: Figure 1 shows a negative trend illustrating the inverse relationship between pcreu and sgexp. How does it illustrate this? You need a description that shows this.

Response 11: In addition to Figure 1, descriptive statistics in Table 3 illustrate a negative relationship between pcreu and sgexp. The pairwise correlation coefficient between these variables is -0.27, which is highly significant.

Comments 12: The test includes the methods of Levin, Lin, Chu, Im, Pesaran, and Sheehan, as well as the extended Dickey-Fuller method. Which method was used in the article and why is it better?

Response 12: Crosssectional Im, Pesaran, and Shin (CIPS) unit root test method was used. This test is not only better suited for scenarios of cross-sectional dependence but also handles heterogeneity effectively. Therefore, the CIPS unit root test was used in this study. See lines 31-38 on page 10.

Comments 13: Long-term and short-term evaluation. Why was it conducted? What did it reveal in relation to the topic of the article?

Response 13: When variables are co-integrated in panel data, they share a long-run equilibrium relationship, even if short-term deviations occur due to shocks like weather or energy price fluctuations. Long-term estimations capture the permanent, stable effects of drivers such as income, demographics, and climate on household energy consumption. In contrast, short-term estimations reveal how quickly households adjust their energy use when facing temporary disruptions. Together, these insights are vital for policy design: they inform long-term strategies like home energy efficiency improvements and short-term interventions such as targeted subsidies during energy price spikes, ensuring a balanced and effective approach to managing residential energy demand.

Comments 14: The title of the article needs to be clarified. The title should be consistent with the research.

Response 14: The title of the article is based on research question and research objective as outline on page 3; lines 26-38. However, the existing title has been changed slightly to “Does government environmental expenditure reduce residential energy consumption in Canada: Evidence from provincial panel data”, which I believe aligns well with the research’s focus and findings.

 Comments 15: The percentage of text uniqueness should be increased.

Response 15: The conceptual idea, methodology, data collection, estimation, and manuscript drafting are entirely the author’s original work, making the manuscript unique.

Comments 16: The layout of the article does not meet the requirements.

Response 16: The article adheres to the standard layout—introduction, literature review, methodology and data source, results, discussions, conclusions, and policy implications—commonly used by most journals including the Sustainability journal. As such, I believe it meets the publication requirements.

 

4. Response to Comments on the Quality of English Language

Point 1: To the best of my understanding, the manuscript reads well and does not contain any grammatical errors. Three other reviewers also noted that the English is fine and does not require any further improvement.

5. Additional clarifications

There are no additional clarifications, as I have responded to and addressed each of the reviewers' comments.

Reviewer 3 Report

Comments and Suggestions for Authors

The novelty of this research lies in its estimation of the impact of government spending on environmental protection by reducing residential energy consumption, thereby improving Canada's environmental quality. The author uses panel data from 10 Canadian provinces over the 1995-2020 period. The empirical methodology correctly combines unit root tests (CIPS), co-integration tests, and FMOLS and DOLS techniques. The main conclusion indicates that government expenditure on environmental protection has effectively reduced residential energy consumption in Canada while increasing energy efficiency.

I only found some minor mistakes and omissions throughout the text. They are as follows:

a) Page 4: Explain what SO2 means (sulfur dioxide).

b) Page 6: Delete the colon at the end of the sentence.

c) Page 7: Is it pcreu or pcrenu?

d) Page 8: Some subscripts are missing in "pit" and in "...the province i in year t". Also, change "heating-dgree" and "cooling-dgree".

e) Page 8: Why could the effect of changes in the elderly population yield ambiguous results?

f) Why did the author not add panel causality tests such as the Dumitrescu and Hurling (2012) panel test or the Kónya (2006) panel test? Of course, the causality would confirm the direction of the effects between the model's variables.

In my opinion, the research has solid conclusions based on the econometric method employed.

 

References

Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014

Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992. https://doi.org/10.1016/j.econmod.2006.04.008

Author Response

Response to Reviewer 3 Comments

1. Summary

We sincerely appreciate and thank the reviewer for their insightful comments and suggestions. Each has been carefully addressed and duly incorporated into the revised manuscript. Please find my detailed responses below, with the corresponding changes clearly highlighted in red in the revised submission.

Open Review

( ) I would not like to sign my review report
(x) I would like to sign my review report

Quality of English Language

( ) The English could be improved to more clearly express the research.
(x) The English is fine and does not require any improvement.

 

2. Questions for General Evaluations	Yes	Can be improved	Must be improved
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	(x)	( )	( )
Are the research design, questions, hypotheses and methods clearly stated?	(x)	( )	( )
Are the arguments and discussion of findings coherent, balanced and compelling?	(x)	( )	( )
For empirical research, are the results clearly presented?	(x)	( )	( )
Is the article adequately referenced?	(x)	( )	( )
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	(x)	( )	( )

3. Point-by-point Response to Comments and Suggestions for Authors

Comments 1: Page 4: Explain what SO2 means (sulfur dioxide).

Response 1: SO2 means sulphur dioxide (see line 11 on page 5).

Comments 2: Page 6: Delete the colon at the end of the sentence.

Response 2: the colon is deleted at the end of the sentence.

Comments 3: Page 7: Is it pcreu or pcrenu?

Response 3: It is pcreu, which is corrected in the manuscript.

Comments 4: Page 8: Some subscripts are missing in "pit" and in "...the province i in year t". Also, change "heating-dgree" and "cooling-dgree".

Response 4: corrected in the manuscript.

Comments 5: Page 8: Why could the effect of changes in the elderly population yield ambiguous results?

Response 5: Some explanations are provided as shown below (see lines 8-10 on page 9).

The impact of changes in the elderly population on household energy consumption can be mixed. While more time at home can drive up energy use, budget constraints and environmental awareness can offset or amplify these effects.

Comments 6: Why did the author not add panel causality tests such as the Dumitrescu and Hurling (2012) panel test or the Kónya (2006) panel test? Of course, the causality would confirm the direction of the effects between the model's variables.

Response 6: Panel cointegration tests, used in this study, identify long-run equilibrium relationships in panel data, such as between energy use and government spending, and are essential for analyzing the impacts of permanent policy or technological changes. While panel cointegration tests are sufficient for long-term analysis, panel causality tests reveal short-term, dynamic effects and the direction of causality, offering complementary insights into how variables interact over time.

Comments 7: In my opinion, the research has solid conclusions based on the econometric method employed.

Response 7: I appreciate the reviewer for sharing their thoughtful opinion.

4. Response to Comments on the Quality of English Language

Point 1: The reviewer noted that "The English is fine and does not require any improvement."

5. Additional clarifications

There are no additional clarifications, as I have responded to and addressed each of the reviewers' comments.

 

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for your dedicated effort in preparing this manuscript. This study investigates whether government expenditure on environmental protection and province-specific environmental policies reduce residential energy consumption in Canada, thereby improving environmental quality. To improve their article, the authors could consider the following major recommendations:

• Investigate the relative effectiveness of different types of province-specific environmental policies (e.g., carbon tax vs. cap-and-trade) in reducing residential energy consumption.
• Include data on household and dwelling attributes (e.g., insulation, appliance efficiency) to better understand their impact on energy consumption. 
• Explore how government environmental spending influences residential energy use efficiency and the adoption of energy-efficient technologies. 
• Conduct comparative studies across provinces to identify best practices and policy frameworks that yield the most significant reductions in energy consumption.
• Examine the role of household behavior and environmental attitudes in shaping energy consumption patterns, especially in response to government policies. 
• Investigate the limited impact of cooling degree days further, considering potential future increases in air conditioning use due to climate change. 
• Study the long-term effects of environmental policies on energy consumption trends, including spillover effects on other sectors. ​
• Analyze why income elasticity for residential energy consumption in Canada is lower compared to other developed countries and explore implications for policy design.
• Examine the nuanced impact of the elderly population on energy consumption, considering factors like budget constraints, environmental awareness, and time spent at home. 
• Address data limitations by compiling more granular datasets, including annual time-series data on household and building characteristics. 
• Advocate for the adoption of explicit environmental policies in provinces currently lacking them, based on the demonstrated effectiveness in reducing energy consumption. 
• Explore how government spending and policies can be tailored to address future climate challenges, such as increased heating or cooling demands.

These recommendations can help the author refine the study and expand its scope for future research

Thank you for your efforts, and I wish you continued success with your work.

Best wishes,

Author Response

Response to Reviewer 4 Comments

1. Summary 

I sincerely thank the reviewer for their insightful comments and suggestions. Each comment has been addressed individually and incorporated into the manuscript where appropriate. Please find the detailed responses below and the corresponding revisions/corrections highlighted in red in the re-submitted files.

Open Review

( ) I would not like to sign my review report
(x) I would like to sign my review report

Quality of English Language

( ) The English could be improved to more clearly express the research.
(x) The English is fine and does not require any improvement.

 

 

2. Questions for General Evaluations	Yes	Can be improved	Must be improved	Not applicable
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	( )	(x)	( )	( )
Are the research design, questions, hypotheses and methods clearly stated?	( )	(x)	( )	( )
Are the arguments and discussion of findings coherent, balanced and compelling?	( )	(x)	( )	( )
For empirical research, are the results clearly presented?	( )	(x)	( )	( )
Is the article adequately referenced?	(x)	( )	( )	( )
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	( )	(x)	( )	( )

3. Point-by-point Response to Comments and Suggestions for Authors

Comments 1: Investigate the relative effectiveness of different types of province-specific environmental policies (e.g., carbon tax vs. cap-and-trade) in reducing residential energy consumption.

Response 1: The panel dataset used in this study (10 provinces × 25 years) captures broad trends in household energy consumption and accounts for some provincial differences. However, because only a few provinces implemented specific policies like carbon taxes or cap-and-trade, there is limited variation to directly compare their effectiveness. Differences in policy timing and implementation details further confound these comparisons. Therefore, it may not be feasible or advisable to use this dataset for comparing the effectiveness of province-specific policies. While examining policy effectiveness is important, such detailed analysis would be better explored in future research with more granular, micro-level household data.

Comments 2: Include data on household and dwelling attributes (e.g., insulation, appliance efficiency) to better understand their impact on energy consumption. 

Response 2: This is a valuable suggestion. Unfortunately, we currently lack time-series data by province on household and dwelling attributes to analyze their impact.

Comments 3: Explore how government environmental spending influences residential energy use efficiency and the adoption of energy-efficient technologies. 

Response 3: This is a valuable suggestion, but our dataset cannot support this analysis. While it includes aggregate household energy consumption and macroeconomic indicators, it does not capture direct measures of energy efficiency, such as appliance upgrades, home insulation, or retrofitting projects. Assessing energy use efficiency (e.g., energy per square foot) and the adoption of energy-efficient technologies (like smart thermostats, heat pumps, or LED lighting) requires micro-level data from household surveys or detailed building data, not provincial aggregates. Government environmental spending data shows provincial funding levels but does not directly link to household adoption or energy efficiency improvements. Future research could explore this area further.

Comments 4: Conduct comparative studies across provinces to identify best practices and policy frameworks that yield the most significant reductions in energy consumption.

Response 4: Using only macro-level panel data (10 provinces over 25 years), it is not feasible to conduct comparative studies across provinces to identify best practices or policy frameworks for reducing residential energy consumption. While the dataset includes provincial environmental expenditure and total energy use, it lacks detailed information on policy design, implementation, and household-level adoption of energy-efficient technologies. Limited variation in policy presence and intensity, as well as confounding factors like geography, climate, housing stock, and local economic conditions, make it difficult to isolate the impact of policies. Moreover, with only 10 provinces, the sample is too small for robust statistical comparisons.

Comments 5: Examine the role of household behavior and environmental attitudes in shaping energy consumption patterns, especially in response to government policies. 

Response 5: Household-level survey data are needed to examine how household behavior and environmental attitudes shape energy consumption patterns, particularly in response to government policies. Unfortunately, we currently lack this data, but future research could pursue this important area of investigation.

Comments 6: Investigate the limited impact of cooling degree days further, considering potential future increases in air conditioning use due to climate change. 

Response 6: While the analysis using current dataset shows no long-run effect of cooling degree-days (CDD), this finding may not hold in the future as climate change is expected to accelerate cooling needs in Canada. Future research should incorporate projections of air conditioning adoption into residential energy demand models, as well as examine regional differences in climate sensitivity and adaptation capacity. Additionally, investigating the equity impacts of increased cooling demand is crucial, along with exploring policy measures to mitigate these effects—such as green building retrofits and incentives for energy-efficient air conditioning.

Comments 7: Study the long-term effects of environmental policies on energy consumption trends, including spillover effects on other sectors. ​

Response 7: Our dataset aggregates provincial-level data, which captures broad trends in household energy consumption but does not provide detailed insights into long-term behavioral or technological shifts that environmental policies might induce. It focuses solely on household energy consumption and does not cover other sectors like transportation or industry, making it impossible to assess potential spillover effects. Although it includes government environmental spending, it lacks detailed data on specific policy measures, timing, and implementation strategies. This limitation makes it difficult to identify the long-term structural changes that policy frameworks may drive.

Comments 8: Analyze why income elasticity for residential energy consumption in Canada is lower compared to other developed countries and explore implications for policy design.

Response 8: Some explanations were provided as shown below (see lines 12-19 on page 16)

In Canada, the lower income elasticity of residential energy consumption reflects the country’s cold climate, which makes space heating essential regardless of income. Stringent building codes and widespread energy-efficient technologies mean that energy use doesn’t increase much as incomes rise, unlike in other countries. Regional climate differences further dampen overall income elasticity. As a result, price-based policies like carbon taxes may have limited direct effects on household energy consumption. Instead, policies should prioritize direct interventions—such as building retrofits, better insulation, and efficient heating systems—while ensuring equitable access to energy efficiency upgrades for lower-income households.

Comments 9: Examine the nuanced impact of the elderly population on energy consumption, considering factors like budget constraints, environmental awareness, and time spent at home. 

Response 9: Some explanations examining the nuanced impact of the elderly population on energy consumption were given as shown below (see lines 20-30 on page 16).

The impact of an increasing elderly population on residential energy consumption is complex and shaped by several factors. Elderly individuals typically spend more time at home, which could drive up energy use for heating, cooling, and appliances. However, this effect can be offset by budget constraints, as older adults often live on fixed incomes and may limit their energy consumption to control costs. Additionally, heightened environmental awareness among the elderly can lead to more energy-conscious behaviors, further reducing consumption. Collectively, these factors create a nuanced impact where the elderly population may even contribute to overall declines in energy use despite spending more time at home.

Comments 10: Address data limitations by compiling more granular datasets, including annual time-series data on household and building characteristics. 

Response 10: The dataset used in this study lacks detailed information on households, dwellings, and policy implementation. Future research could address these limitations by collecting more granular household-level data through surveys, including energy consumption patterns, appliance ownership, building characteristics, and energy-saving behaviors. Additionally, incorporating specific policy data (like rebates and retrofitting incentives) and granular weather and climate variables would help capture broader environmental influences. Longitudinal data would also enhance understanding of how households adapt over time, including variations by income, age, and household composition. See lines 4-11 on page 17 of the text.

Comments 11: Advocate for the adoption of explicit environmental policies in provinces currently lacking them, based on the demonstrated effectiveness in reducing energy consumption. 

Response 11: Thanks for the comment. The adoption of explicit environmental policies in provinces currently lacking them were advocated as shown below (see lines 9-16 on page 18).

Given the demonstrated effectiveness of province-specific environmental policies in significantly reducing household energy consumption, it is important to advocate for the adoption of explicit environmental policies in provinces currently lacking them. Implementing tailored measures such as carbon taxes, building retrofits, or energy efficiency incentives can provide substantial reductions in household energy demand, contributing to both climate action and economic resilience. These policy efforts will help ensure that all provinces participate in the transition to sustainable energy use, leveraging the clear benefits already seen in provinces with robust environmental initiatives.

Comments 12: Explore how government spending and policies can be tailored to address future climate challenges, such as increased heating or cooling demands.

Response 12: Thanks for the comment. How government environmental expenditure and policies can be tailored to address future climate challenges were explored as shown below (see lines 33-40 on page 18).

Future climate challenges—like increased heating and cooling demands—underscore the importance of tailoring government spending and policies to promote energy-efficient technologies and building retrofits. Investing in insulation improvements, smart thermostats, and adaptive technologies will help households manage rising energy needs more sustainably. Policy incentives can also target energy equity, ensuring low-income households have access to these upgrades. Additionally, integrating climate resilience strategies—such as shading, passive cooling designs, and renewable heating solutions—into building codes and government programs will further prepare residential energy systems for a warming climate.

Comments 13: These recommendations can help the author refine the study and expand its scope for future research.

Response 13: I fully agree with the reviewer’s observations. However, due to data limitations, we are unable to implement all of the recommendations, as previously explained.

Comments 14: Thank you for your efforts, and I wish you continued success with your work.

Response 14: Many thanks for the compliment.

4. Response to Comments on the Quality of English Language

Point 1: The reviewer noted that "The English is fine and does not require any improvement."

5. Additional clarifications

There are no additional clarifications, as I have responded to and addressed each of the reviewers' comments.

 

 

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Topic is important, update of literature review is helpful, if possible numbers on the impact of financial incentives and energy performance standards impact would be helpful, especially as a negative impact in South Africa was observed. Maybe %age reduction of carbon emissions caused by initiatives can be shown. please redraw figure 1. A screenshot does not fit the standards of the journal. Does figure 1 indicate that in provinces with explicit government policies energy consumption per capita was 10% lower? Is this corrected by climate (heating degree days) and land use characteristics (percentage of people living in urban environment?). Can you indicate the names of the provinces with lower consumption? The conclusions should also reflect comparable effects, not only age of population and give. Please also give a range to expect for t carbon reduction per US$ spent by the government.

Author Response

Response to Reviewer 1 Comments (Round 2)

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses (point-by-point) below and the corresponding revisions highlighted in red in the re-submitted file.

Comments 1: Topic is important, update of literature review is helpful, if possible numbers on the impact of financial incentives and energy performance standards impact would be helpful, especially as a negative impact in South Africa was observed.

Response 1: We sincerely thank the reviewer for this comment. The literature review has been updated to include the impact of financial incentive and energy performance standard, including the negative effects observed in South Africa, as shown below and in the manuscript see lines 36-39 on page 4 & lines 1-12 on page 5

A body of research highlights the role of fiscal and financial incentives in promoting energy efficiency (EE) and green building adoption. Simpeh and Smallwood [16] present a framework for South Africa that integrates financial incentives with recognition and support to align stakeholder interests. Blommestein and Daim [17] emphasize that while financial savings drive consumer adoption of energy-efficient devices, environmental awareness and product performance are also influential. In Portugal, Koengkan et al. [18] find that higher income does not necessarily lead to better energy performance, but access to consumer credit and fiscal incentives like tax rebates significantly boost the adoption of high-efficiency homes. On a broader scale, Sloot and Scheibehenne [19] show that financial incentives modestly reduce total energy use (-1.83%) but substantially cut peak electricity demand (-10%), particularly when paired with enabling technologies like smart meters. Studies on South Africa reveal that existing financial incentives often fail to effectively stimulate energy efficiency. A study of major listed businesses showed that although tax incentives are available for energy efficiency and renewable energy investments, they were not perceived as sufficiently motivating; non-tax factors were more influential in decision-making and compliance burdens were seen as deterrents Dippenaar [20].

Comments 2:  Maybe %age reduction of carbon emissions caused by initiatives can be shown.

Response 2: Thank you to the reviewer for this valuable comment. It has been addressed as shown below and on lines 12-15 on page 16 in the text.

This estimate indicates that, on average, government environmental expenditure reduces residential energy consumption by 437,456 MWh per year across the ten Canadian provinces. Assuming coal-fired power generation, this reduction translates into approximately 0.06% of Canada’s annual CO₂e emissions.

Comments 3: please redraw figure 1. A screenshot does not fit the standards of the journal.

Response 3: Figure 1 was not a screenshot. However, I have redrawn it using Stata software (see the title of figure 1 on page 12).

Comments 4: Does figure 1 indicate that in provinces with explicit government policies energy consumption per capita was 10% lower? Is this corrected by climate (heating degree days) and land use characteristics (percentage of people living in urban environment?).

Response 4: No. Figure 1 illustrates the inverse relationship between per capita residential energy use (pcreu) and the share of government environmental expenditure (sgexp), the key policy variable of interest. This relationship is based on partial correlation, without controlling for factors such as climate, land use characteristics, and urbanization. It is important to note that all ten provinces allocate spending for environmental protection, though to varying degrees.

Comments 5: The conclusions should also reflect comparable effects, not only age of population and give. Please also give a range to expect for t carbon reduction per US$ spent by the government.

Response 5: Another valuable comment, and I thank the reviewer. Comparable effects have been incorporated into the conclusion, as shown below and on lines 19-27 on page 19 of the text.

This study also found that both price and income elasticities reflect inelastic responses of residential energy consumption to changes in price and income. By contrast, an increase in the share of the elderly population was associated with reduced household energy consumption, while heating degree days emerged as the most significant factor influencing energy use. Specifically, heating degree days exert the greatest impact on per capita residential energy consumption in Canada, followed by government environmental expenditure, with energy price having the least influence. Based on our estimates, government spending leads to carbon reductions ranging from 4.3 kg to 5.75 kg CO₂e per dollar spent. These findings highlight the effectiveness of government expenditure on environmental protection in encouraging pro-environmental behavior and reducing household energy consumption.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Comments on the article have been corrected. 
Comprehensive answers have been provided.

Author Response

This reviewer does not have any additional comments to response in round 2

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Authors,

I would like to thank the author for their detailed and thoughtful responses to the reviewer comments.

The author has adequately acknowledged the limitations of the current dataset and clearly explained the constraints preventing the incorporation of additional analyses. Given the scope and structure of the data—specifically the macro-level panel across Canadian provinces—it is understandable that certain valuable lines of inquiry (e.g., policy comparisons, household-level behaviors, and energy efficiency measures) could not be fully addressed within this study.

After a careful review of both the manuscript and the author’s responses, I am satisfied that the paper meets the academic standards for publication. Therefore, I recommend acceptance in its current format, provided the above clarification on limitations and future research is addressed in the final version.

Sincerely,

Author Response

This reviewer satisfied with my previous responses (in round 1) and does not have any additional comments in round 2 to address