the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Feb 2022
Are the Rich less Prone to Flooding? A Case Study on Flooding in the Southern Taiwan during Typhoon Morakot and Typhoon Fanapi
Abstract. The study uses Taiwan as an example to explore whether the budget allocation of risk reduction depends on income-related political power. Specifically, we empirically examine the effect of household income on the probability of flooding. Beginning in 2006, the government implemented an 8-year project referred to as the “Regulation Project for Flood-Prone Areas” with a budget of NT$115.9 billion (US$3.86 billion). Over half of the budget was allocated to local authorities in southern Taiwan to help them carry out flood risk mitigation projects. As it was not clear how the local authorities set their priorities in allocating their budgets, this study investigates whether high-income individuals may have used their political influence to influence the budget allocation to improve the flood risk reduction facilities in their communities. Villages, whose average household income was within the top 10 % in the county or city, were selected as high-income villages and assigned to the treatment group, whereas other villages were included in the control group. The results using propensity score matching (PSM) show that the flood probability of the high-income group (13 % and 16.9 %, respectively) was lower than that of low-income group (22 % and 28 %) during Typhoon Morakot and Typhoon Fanapi, suggesting that high-income areas are less prone to flooding, which might stem from their political power.
- Preprint
(556 KB) - Metadata XML
- BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on nhess-2022-38', Anonymous Referee #1, 18 Mar 2022
This study aims at providing evidence that income affects flood risk mitigation. The authors claim that their analysis shows that this is the case. I have great problems with this conclusion.
The authors state that “high-income individuals may have used their political influence to influence the budget allocation to improve the flood risk reduction facilities in their communities” (Abstract, and Page 15, Lines 286-287). That is quite a statement, that requires strong evidence. The statement would require that 1) flood risk has actually decreased in those high-income areas, and 2) that the flood risk also has been reduced *more* in areas with higher incomes, compared to areas with lower incomes. However, neither of these is shown in the analysis.
The only thing the authors show is that there is a difference between income and flood risk. But this is well-known from past research in developed countries as well as developing countries. Lower income households settle in locations that are more flood prone, for several reasons, often a higher flood risk also leads to lower property prices, leading to poorer populations to move here.
I do not doubt that mechanisms of political influence, and nontransparent processes are at play in Taiwan. However, the current study simply cannot deny or confirm any of that to have an effect on actual reduction of flood risk.
Answering the central claim from the paper would require an analysis of the flood hazard before and after the programme, to analyse whether there is any *difference* in flood risk reduction for the different income groups. So how was the flood probability of the communities before the programme that started in 2008? The authors cannot show that.
In Tables 5 and 6, in fact some of the effects of the location choices that I refer to can be seen. In particular, elevation plays a role here (and is related to flood probability, as seen in Table 4), with the low-income group having a lower elevation, and thus potentially a higher flood hazard.
Also, I wonder about the uncertainty of the flood probability estimates. The authors report that this is collected from self-reports (Line 198), but how could this affect the analysis?
Additionally, the authors cannot exclude the possibility that floods from typhoons had effects on income, as they suggest also themselves on page 3 (Lines 84-87). Although the income data is from 2006, the authors also report that several typhoons hit Taiwan every year, and such impacts could affect incomes, so this could in fact be an additional factor, as shown also in other studies (e.g. https://doi.org/10.1016/j.ecolecon.2020.106879 and https://doi.org/10.1016/j.jenvman.2022.114852).
Finally, I have reservations about whether the programme has led to such investments that there would be a noticeable effect on flood risk for these two specific events. $3.86 billion seems a lot, but it also seems this was spent on quite a large area, and both events were quite extreme.
Moreover, the limited description seems to imply that most of the implemented measures would actually benefit several riparian communities, such as “construction works” that suggests structural flood protection, such as levees and reservoirs. Or are there any engineering reasons why the measures would have benefitted certain geographic locations, and not others? The current description is highly suggestive (Lines 54-70), but lacks factual descriptions of what investments and construction works were made.
In sum, I think the main conclusion from the paper is not supported by the research design and the results. The authors only show that the lower income communities have a higher flood risk.
Citation: https://doi.org/10.5194/nhess-2022-38-RC1 -
AC1: 'Reply on RC1', Yen-Lien Kuo, 22 Mar 2022
We did not have the flooding probability of villages before the project. However, as the title of this study, we did prove that those 2006 high income (10%) villages had less flooding probability than 2006 non-high income villages during 2009 and 2010 typhoons in Southern Taiwan. Rent-seeking is one of the reasonable and possible mechanism because the village’s rainfall is totally exogenous and the rainfall, terrain, population, and house price of the village were paired by PSM to be no significant difference between high income and non-high income villages. We had used T-test to check the mean difference of variables of treatment group and control group was insignificant including elevation. The T-test results can be added to be an appendix. Rubin’s B and Rubin’s R were also adopted to check the balance of matching and fitted with its standard. Since the risk reduction efforts toward more population and high real estate price area are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is a possible mechanism.
Concerning flooding causing migration, the difference in income growth rates between 2006 to 2016 of flood-prone villages (flooded both during 2009 typhoon Morakot and 2010 typhoon Fanapi) and non flood-prone villages were insignificant. Please check Page7, Lines 173-178. As flooding does not seem to be a significant factor affecting income and the relocation of the residents of the flooded villages in Taiwan.
Concerning flooding reducing income, typhoons in 2009 and 2010 can deteriorate 2006 income. Besides, the following losses estimation and the victim's survey of Typhoon Morakot showed the damages suffered by victim households were not huge.
“There were 140,424 households with flooding depths of more than 50 cm during Typhoon Morakot according to an investigation report conducted by the Typhoon Morakot Post-Disaster Reconstruction Commission of the Executive Yuan, Taiwan. A total of NT$5.31 billion in damages nationwide and an average of NT$37,814 per household were caused by Typhoon Morakot according to the 2009 annual report of the NCDR. Comparing those to the average annual household income of NT$1,074,180 in 2009, the damages suffered by victim households were not huge. Lastly, changes in income after the disaster were investigated. According to the "Social Impacts and Recovery Survey of Typhoon Morakot (Phase 1)" conducted by the NCDR, where a questionnaire survey was carried out on Typhoon Morakot victims (i.e. households whose houses were so severely damaged that they had become uninhabitable), income of 56% of the victims remained unchanged, whereas 17.9% of the victims showed income increases and 25.4% income decreases. The unemployment rate of the affected households increased by 4.2%. Overall, flooding did not cause too severe an impact on household income.”
Those two events were quite extreme. Typhoon Morakot is the most serious typhoon (the highest losses) in the history of Taiwan. Nevertheless, 2009 and 2010 typhoons cannot affect 2006 income. Besides, the losses caused by other smaller events during 2006 to 2010 were much smaller than that by typhoon Morakot. The above description can be added to the manuscript.
More than half of the total budget of the Project was provided to these southern parts of Taiwan. The budget was mainly for structural flood protection, such as levees, pumping stations, and detention ponds. Almost all rivers already had some sort of levees before the project. Due to the Project, the local governments decided the priority and the allocation of enhancing levees and building detention ponds. We used a community/village which is the lowest administrative entity to have a large sample size.
At least, studies of social vulnerability to flooding concerned the poor but this study analyzed 10% high income villages. PSM had been adopted for the first time to find villages with similar rainfall, population, house price, and terrain, and found that high income villages are less prone to flooding during 2009 and 2010 typhoons.
Citation: https://doi.org/10.5194/nhess-2022-38-AC1 -
RC3: 'Reply on AC1', Anonymous Referee #1, 13 May 2022
I would like to thank the authors for responding to my review. I would like to raise a few points, that were addressed in their response text.First, the auhtors state that "Rent-seeking is one of the reasonable and possible mechanism because the village’s rainfall is totally exogenous and the rainfall, terrain, population, and house price of the village were paired by PSM to be no significant difference between high income and non-high income villages." First of all, I think that rent-seeking is a term used normally for more direct benefits from e.g. subsidies, or other special treatment by the government. I am not sure if benefits of risk reduction investments really includes this. But I am not an economist.Second, the term "migration" is not mentioned by me. I am not sure why the authors bring this up.In this context, I would encourage the author to write a comment-by-comment response. I found it very hard to see how they have respond to each of my (and the other reviewers) comments.Third, the authors write that: "more population and high real estate price area are democratic and economic (cost- benefit analysis) mechanisms, respectively, rent-seeking is a possible mechanism."I have two issues with this statement. If the process was democratic, then rent seeking would not be a problem. This seems to contradict the main statement from the authors, that the process is in fact not doing justice to welfare, or equity, and is therefore not democratic.Also, real estate prices are not used for cost-benefit analyses to decide on measures to reduce risks from natural hazard. It is damage costs, or more precisely, replacement and repair costs in the event of a flood. This is highly problematic, as I also write in my comment on the author response to RC2, below.Fourth, and finally, the authors write that "Concerning flooding reducing income, typhoons in 2009 and 2010 can deteriorate 2006 income". But then later: "Nevertheless, 2009 and 2010 typhoons cannot affect 2006 income." I am not sure if I can follow that.The mere adjustment of the title of the paper is also not sufficient. The authors should indicate how they can support the rent-seeking argument with their analysis. The current responses do not make a sufficient argument for that, nor do the authors indicate how they think they can adjust the manuscript text to account for this main comment.Citation: https://doi.org/
10.5194/nhess-2022-38-RC3 - AC3: 'Reply on RC3', Yen-Lien Kuo, 24 May 2022
-
RC3: 'Reply on AC1', Anonymous Referee #1, 13 May 2022
- AC5: 'Reply on RC1', Yen-Lien Kuo, 28 May 2022
-
AC1: 'Reply on RC1', Yen-Lien Kuo, 22 Mar 2022
-
RC2: 'Comment on nhess-2022-38', Anonymous Referee #2, 03 May 2022
-
AC2: 'Reply on RC2', Yen-Lien Kuo, 09 May 2022
It is intuitive that the motivation is the flood risk reduction in their residing areas when the local governments decided the priority and the allocation of public flood protections. However, the advantage of high income people and their political power is difficult to prove because that works under the table. We can only prove that through the outcome. We used the lowest administrative entity (villages) during extreme typhoon cases to have the data on residents’ income and large sample size. Since we need widespread flooding to do this empirical study, the non-extreme typhoon cases are not suitable. Extreme cases seldom happen. Currently, we did not have the flooding probability of villages before the project. However, this study did proof that those 2006 high income (10%) villages had less flooding probability than 2006 non-high income villages during 2009 and 2010 typhoons in Southern Taiwan. Therefore, the topic of this paper can be changed to ‘Are the Rich less Prone to Flooding during Typhoon Morakot and Typhoon Fanapi in the Southern Taiwan?’. I may point out this research limitation at the end of this paper.
The budget was mainly for structural flood protection, such as levees, pumping stations, and detention ponds. Almost all rivers already had some sort of levees before the project. Due to the Project, the local governments decided the priority and the allocation of enhancing levees and building detention ponds. The decision process had been described in the manuscript. The content of the Project can be added to the manuscript.
In Taiwan, the flooding is mainly inundation which is caused by extreme rainfall and insufficient drainage rather than river flooding. Even during extreme typhoons like Morakot and Fanapi, most of the casualty was not from flooding (mainly because of landslides). In Taiwan, seismic safety is emphasized in the commercials of high price buildings rather than flood prevention because the drainage is managed and regulated by the government.
We put the house price in the model and the hypothesis of that is negative because the house price is usually adopted to measure the benefit of public flood protection measures called the hedonic price method. It is a mechanism of cost-benefit analysis which leads public flood protection to the areas where high price buildings are located. Since the risk reduction efforts toward more population and high real estate price areas are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is the most possible mechanism.
The data sources of flooding investigations of those two typhoons were stated in the manuscript. The process of flooding investigation is that the flooding locations (point) were reported by residents and then the investigation team of each city/county went to check and plotted the flooding area. However, since each team had a different format of records, the flood depth was not recorded in some cities/counties (only areas). The minimum recorded flood depth is 20cm from the team that recorded flood depth. The recorded flood depth will be added to the manuscript. In line 107 of page 4, all villages in Pingtung county, Kaohsiung city, and Tainan city were adopted in this study. There is no criteria for the inclusion of villages. The altitude (elevation) and slop were adopted to control the nature of villages.
Citation: https://doi.org/10.5194/nhess-2022-38-AC2 -
RC4: 'Reply on AC2', Anonymous Referee #1, 13 May 2022
I do not want to preclude any responses from this reviewer, but I felt I need to comment here also, after reading the response from the authors.Here I also have some issues. First, the fundamental point I raised is whether the authors actually show proof of the outcome of the different treatment of wealthy and non-wealthy populations in Taiwan.Now the authors write: "However, the advantage of high income people and their political power is difficult to prove because that works under the table. We can only prove that through the outcome." So here we agree, but I still do not see the evidence, in this case the actual outcome in terms of higher risk reduction in wealthy areas before and after 2006.Also here, it would be better when the authors address comment-by comment.And then the authors write: "house price is usually adopted to measure the benefit of public flood protection measures called the hedonic price method. It is a mechanism of cost-benefit analysis which leads public flood protection to the areas where high price buildings are located."House prices are not used in CBA. Neither is hedonic pricing. It is replacement and repair costs, which only differ between wealthy and non-wealthy neighbourhoods, in as far as houses are larger and more luxurious. This is only partly reflected in housing prices. And house size is usually accounted for in CBA. Location is much more important for house prices than construction costs.And this statement also got me confused: "Since the risk reduction efforts toward more population and high real estate price areas are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is the most possible mechanism."If the process is democratic and CBA-based, there would not be rent-seeking to the extent that it influences flood risk. I still do not see how the authors could argue otherwise.Citation: https://doi.org/
10.5194/nhess-2022-38-RC4 - AC4: 'Reply on RC4', Yen-Lien Kuo, 24 May 2022
-
RC4: 'Reply on AC2', Anonymous Referee #1, 13 May 2022
- AC6: 'Reply on RC2', Yen-Lien Kuo, 28 May 2022
-
AC2: 'Reply on RC2', Yen-Lien Kuo, 09 May 2022
Status: closed
-
RC1: 'Comment on nhess-2022-38', Anonymous Referee #1, 18 Mar 2022
This study aims at providing evidence that income affects flood risk mitigation. The authors claim that their analysis shows that this is the case. I have great problems with this conclusion.
The authors state that “high-income individuals may have used their political influence to influence the budget allocation to improve the flood risk reduction facilities in their communities” (Abstract, and Page 15, Lines 286-287). That is quite a statement, that requires strong evidence. The statement would require that 1) flood risk has actually decreased in those high-income areas, and 2) that the flood risk also has been reduced *more* in areas with higher incomes, compared to areas with lower incomes. However, neither of these is shown in the analysis.
The only thing the authors show is that there is a difference between income and flood risk. But this is well-known from past research in developed countries as well as developing countries. Lower income households settle in locations that are more flood prone, for several reasons, often a higher flood risk also leads to lower property prices, leading to poorer populations to move here.
I do not doubt that mechanisms of political influence, and nontransparent processes are at play in Taiwan. However, the current study simply cannot deny or confirm any of that to have an effect on actual reduction of flood risk.
Answering the central claim from the paper would require an analysis of the flood hazard before and after the programme, to analyse whether there is any *difference* in flood risk reduction for the different income groups. So how was the flood probability of the communities before the programme that started in 2008? The authors cannot show that.
In Tables 5 and 6, in fact some of the effects of the location choices that I refer to can be seen. In particular, elevation plays a role here (and is related to flood probability, as seen in Table 4), with the low-income group having a lower elevation, and thus potentially a higher flood hazard.
Also, I wonder about the uncertainty of the flood probability estimates. The authors report that this is collected from self-reports (Line 198), but how could this affect the analysis?
Additionally, the authors cannot exclude the possibility that floods from typhoons had effects on income, as they suggest also themselves on page 3 (Lines 84-87). Although the income data is from 2006, the authors also report that several typhoons hit Taiwan every year, and such impacts could affect incomes, so this could in fact be an additional factor, as shown also in other studies (e.g. https://doi.org/10.1016/j.ecolecon.2020.106879 and https://doi.org/10.1016/j.jenvman.2022.114852).
Finally, I have reservations about whether the programme has led to such investments that there would be a noticeable effect on flood risk for these two specific events. $3.86 billion seems a lot, but it also seems this was spent on quite a large area, and both events were quite extreme.
Moreover, the limited description seems to imply that most of the implemented measures would actually benefit several riparian communities, such as “construction works” that suggests structural flood protection, such as levees and reservoirs. Or are there any engineering reasons why the measures would have benefitted certain geographic locations, and not others? The current description is highly suggestive (Lines 54-70), but lacks factual descriptions of what investments and construction works were made.
In sum, I think the main conclusion from the paper is not supported by the research design and the results. The authors only show that the lower income communities have a higher flood risk.
Citation: https://doi.org/10.5194/nhess-2022-38-RC1 -
AC1: 'Reply on RC1', Yen-Lien Kuo, 22 Mar 2022
We did not have the flooding probability of villages before the project. However, as the title of this study, we did prove that those 2006 high income (10%) villages had less flooding probability than 2006 non-high income villages during 2009 and 2010 typhoons in Southern Taiwan. Rent-seeking is one of the reasonable and possible mechanism because the village’s rainfall is totally exogenous and the rainfall, terrain, population, and house price of the village were paired by PSM to be no significant difference between high income and non-high income villages. We had used T-test to check the mean difference of variables of treatment group and control group was insignificant including elevation. The T-test results can be added to be an appendix. Rubin’s B and Rubin’s R were also adopted to check the balance of matching and fitted with its standard. Since the risk reduction efforts toward more population and high real estate price area are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is a possible mechanism.
Concerning flooding causing migration, the difference in income growth rates between 2006 to 2016 of flood-prone villages (flooded both during 2009 typhoon Morakot and 2010 typhoon Fanapi) and non flood-prone villages were insignificant. Please check Page7, Lines 173-178. As flooding does not seem to be a significant factor affecting income and the relocation of the residents of the flooded villages in Taiwan.
Concerning flooding reducing income, typhoons in 2009 and 2010 can deteriorate 2006 income. Besides, the following losses estimation and the victim's survey of Typhoon Morakot showed the damages suffered by victim households were not huge.
“There were 140,424 households with flooding depths of more than 50 cm during Typhoon Morakot according to an investigation report conducted by the Typhoon Morakot Post-Disaster Reconstruction Commission of the Executive Yuan, Taiwan. A total of NT$5.31 billion in damages nationwide and an average of NT$37,814 per household were caused by Typhoon Morakot according to the 2009 annual report of the NCDR. Comparing those to the average annual household income of NT$1,074,180 in 2009, the damages suffered by victim households were not huge. Lastly, changes in income after the disaster were investigated. According to the "Social Impacts and Recovery Survey of Typhoon Morakot (Phase 1)" conducted by the NCDR, where a questionnaire survey was carried out on Typhoon Morakot victims (i.e. households whose houses were so severely damaged that they had become uninhabitable), income of 56% of the victims remained unchanged, whereas 17.9% of the victims showed income increases and 25.4% income decreases. The unemployment rate of the affected households increased by 4.2%. Overall, flooding did not cause too severe an impact on household income.”
Those two events were quite extreme. Typhoon Morakot is the most serious typhoon (the highest losses) in the history of Taiwan. Nevertheless, 2009 and 2010 typhoons cannot affect 2006 income. Besides, the losses caused by other smaller events during 2006 to 2010 were much smaller than that by typhoon Morakot. The above description can be added to the manuscript.
More than half of the total budget of the Project was provided to these southern parts of Taiwan. The budget was mainly for structural flood protection, such as levees, pumping stations, and detention ponds. Almost all rivers already had some sort of levees before the project. Due to the Project, the local governments decided the priority and the allocation of enhancing levees and building detention ponds. We used a community/village which is the lowest administrative entity to have a large sample size.
At least, studies of social vulnerability to flooding concerned the poor but this study analyzed 10% high income villages. PSM had been adopted for the first time to find villages with similar rainfall, population, house price, and terrain, and found that high income villages are less prone to flooding during 2009 and 2010 typhoons.
Citation: https://doi.org/10.5194/nhess-2022-38-AC1 -
RC3: 'Reply on AC1', Anonymous Referee #1, 13 May 2022
I would like to thank the authors for responding to my review. I would like to raise a few points, that were addressed in their response text.First, the auhtors state that "Rent-seeking is one of the reasonable and possible mechanism because the village’s rainfall is totally exogenous and the rainfall, terrain, population, and house price of the village were paired by PSM to be no significant difference between high income and non-high income villages." First of all, I think that rent-seeking is a term used normally for more direct benefits from e.g. subsidies, or other special treatment by the government. I am not sure if benefits of risk reduction investments really includes this. But I am not an economist.Second, the term "migration" is not mentioned by me. I am not sure why the authors bring this up.In this context, I would encourage the author to write a comment-by-comment response. I found it very hard to see how they have respond to each of my (and the other reviewers) comments.Third, the authors write that: "more population and high real estate price area are democratic and economic (cost- benefit analysis) mechanisms, respectively, rent-seeking is a possible mechanism."I have two issues with this statement. If the process was democratic, then rent seeking would not be a problem. This seems to contradict the main statement from the authors, that the process is in fact not doing justice to welfare, or equity, and is therefore not democratic.Also, real estate prices are not used for cost-benefit analyses to decide on measures to reduce risks from natural hazard. It is damage costs, or more precisely, replacement and repair costs in the event of a flood. This is highly problematic, as I also write in my comment on the author response to RC2, below.Fourth, and finally, the authors write that "Concerning flooding reducing income, typhoons in 2009 and 2010 can deteriorate 2006 income". But then later: "Nevertheless, 2009 and 2010 typhoons cannot affect 2006 income." I am not sure if I can follow that.The mere adjustment of the title of the paper is also not sufficient. The authors should indicate how they can support the rent-seeking argument with their analysis. The current responses do not make a sufficient argument for that, nor do the authors indicate how they think they can adjust the manuscript text to account for this main comment.Citation: https://doi.org/
10.5194/nhess-2022-38-RC3 - AC3: 'Reply on RC3', Yen-Lien Kuo, 24 May 2022
-
RC3: 'Reply on AC1', Anonymous Referee #1, 13 May 2022
- AC5: 'Reply on RC1', Yen-Lien Kuo, 28 May 2022
-
AC1: 'Reply on RC1', Yen-Lien Kuo, 22 Mar 2022
-
RC2: 'Comment on nhess-2022-38', Anonymous Referee #2, 03 May 2022
-
AC2: 'Reply on RC2', Yen-Lien Kuo, 09 May 2022
It is intuitive that the motivation is the flood risk reduction in their residing areas when the local governments decided the priority and the allocation of public flood protections. However, the advantage of high income people and their political power is difficult to prove because that works under the table. We can only prove that through the outcome. We used the lowest administrative entity (villages) during extreme typhoon cases to have the data on residents’ income and large sample size. Since we need widespread flooding to do this empirical study, the non-extreme typhoon cases are not suitable. Extreme cases seldom happen. Currently, we did not have the flooding probability of villages before the project. However, this study did proof that those 2006 high income (10%) villages had less flooding probability than 2006 non-high income villages during 2009 and 2010 typhoons in Southern Taiwan. Therefore, the topic of this paper can be changed to ‘Are the Rich less Prone to Flooding during Typhoon Morakot and Typhoon Fanapi in the Southern Taiwan?’. I may point out this research limitation at the end of this paper.
The budget was mainly for structural flood protection, such as levees, pumping stations, and detention ponds. Almost all rivers already had some sort of levees before the project. Due to the Project, the local governments decided the priority and the allocation of enhancing levees and building detention ponds. The decision process had been described in the manuscript. The content of the Project can be added to the manuscript.
In Taiwan, the flooding is mainly inundation which is caused by extreme rainfall and insufficient drainage rather than river flooding. Even during extreme typhoons like Morakot and Fanapi, most of the casualty was not from flooding (mainly because of landslides). In Taiwan, seismic safety is emphasized in the commercials of high price buildings rather than flood prevention because the drainage is managed and regulated by the government.
We put the house price in the model and the hypothesis of that is negative because the house price is usually adopted to measure the benefit of public flood protection measures called the hedonic price method. It is a mechanism of cost-benefit analysis which leads public flood protection to the areas where high price buildings are located. Since the risk reduction efforts toward more population and high real estate price areas are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is the most possible mechanism.
The data sources of flooding investigations of those two typhoons were stated in the manuscript. The process of flooding investigation is that the flooding locations (point) were reported by residents and then the investigation team of each city/county went to check and plotted the flooding area. However, since each team had a different format of records, the flood depth was not recorded in some cities/counties (only areas). The minimum recorded flood depth is 20cm from the team that recorded flood depth. The recorded flood depth will be added to the manuscript. In line 107 of page 4, all villages in Pingtung county, Kaohsiung city, and Tainan city were adopted in this study. There is no criteria for the inclusion of villages. The altitude (elevation) and slop were adopted to control the nature of villages.
Citation: https://doi.org/10.5194/nhess-2022-38-AC2 -
RC4: 'Reply on AC2', Anonymous Referee #1, 13 May 2022
I do not want to preclude any responses from this reviewer, but I felt I need to comment here also, after reading the response from the authors.Here I also have some issues. First, the fundamental point I raised is whether the authors actually show proof of the outcome of the different treatment of wealthy and non-wealthy populations in Taiwan.Now the authors write: "However, the advantage of high income people and their political power is difficult to prove because that works under the table. We can only prove that through the outcome." So here we agree, but I still do not see the evidence, in this case the actual outcome in terms of higher risk reduction in wealthy areas before and after 2006.Also here, it would be better when the authors address comment-by comment.And then the authors write: "house price is usually adopted to measure the benefit of public flood protection measures called the hedonic price method. It is a mechanism of cost-benefit analysis which leads public flood protection to the areas where high price buildings are located."House prices are not used in CBA. Neither is hedonic pricing. It is replacement and repair costs, which only differ between wealthy and non-wealthy neighbourhoods, in as far as houses are larger and more luxurious. This is only partly reflected in housing prices. And house size is usually accounted for in CBA. Location is much more important for house prices than construction costs.And this statement also got me confused: "Since the risk reduction efforts toward more population and high real estate price areas are democratic and economic (cost-benefit analysis) mechanisms, respectively, rent-seeking is the most possible mechanism."If the process is democratic and CBA-based, there would not be rent-seeking to the extent that it influences flood risk. I still do not see how the authors could argue otherwise.Citation: https://doi.org/
10.5194/nhess-2022-38-RC4 - AC4: 'Reply on RC4', Yen-Lien Kuo, 24 May 2022
-
RC4: 'Reply on AC2', Anonymous Referee #1, 13 May 2022
- AC6: 'Reply on RC2', Yen-Lien Kuo, 28 May 2022
-
AC2: 'Reply on RC2', Yen-Lien Kuo, 09 May 2022
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 680 | 310 | 58 | 1,048 | 42 | 43 |
- HTML: 680
- PDF: 310
- XML: 58
- Total: 1,048
- BibTeX: 42
- EndNote: 43
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1