Climate Change Vulnerability, Adaptation, and Feedback Hypothesis: A Comparison of Lower-Middle, Upper-Middle, and High-Income Countries

We appreciate the time and efforts of the editor and reviewers in reviewing the manuscript. We have tried our best to address all issues indicated by the reviewer and believed that the revised version will meet the publication requirements.

REVIEWER # 1

Reviewers comments

Author(s) response*

Point 1: On what ground basis, authors is calming that climate change will impact only developing countries and under developed countries as compares to developed countries, as the climate change is around the globe.

Thank you, reviewer, for the input. The following paragraph from the introduction section provides the ground basis for proving our claim that climate change will impact only developing countries and underdeveloped countries as compared to developed ones.

Low-income countries are considered more vulnerable to climate variability as compared to rich countries (World Bank, 2013; Fankhauser, 2017). Lower and upper-middle-income countries have a lower ability to deal with climate stress because they have insufficient economic, institutional and financial capacity to adapt efficiently (Millner and Dietz, 2014). Whereas, developed countries are less vulnerable to climate change because of their well-established economies, rich capital resources, good governance, and timely and effective readiness strategies (Sarkodie & Strezov, 2019).

We have added it in the introduction from line number 44 to 53 and also in section 3.3.3. Economic Growth

Point 2: Abstract of the paper lacking in terms of results discussion, highlights the impact of the study, can be modified according the results of the works.

This has been revised as instructed.

Point 3: Adaptation readiness and adaptive capacity, difference, need explanation.

We have tried to explain the difference in detail in accordance with your suggestion (Lines 75-106).

Point 4: How Adaptive capacity index is calculated and factors behind the adaptation capacity index

The adaptive capacity index as influencing adaptation to climate change appears in the ND-GAIN country data from the University of Notre Dame Global Adaptation Initiative. The ND-GAIN country index is constructed from 36 vulnerability indicators and nine readiness indicators for up to 192 nations over varying time periods concluding in 2019. The vulnerability indicators consist of 12 Adaptive capacity indicators. The selection of data is guided by the observation that ND-GAIN defines adaptive capacity as “the ability of society and its supporting sectors to adjust to reduce potential damage and to respond to the negative consequences of climate events.

We have tried to explain it in section 3.2. Model Specification and Data

Point 5: How Adaptation readiness index is calculated and factors behind the adaptation capacity index

Adaptation readiness index data was taken from the Notre Dame Global Adaptation Initiatives (ND-GAIN) defines adaptation readiness as preparedness to make effective use of investments for adaptation actions, and it measures adaptation readiness with three components: economic readiness, governance readiness and social readiness. The indicator’s interpretation and definition are also given in section 3.2. Model Specification and Data

6: Caption of figure and tables are not informative

We have made the correction in the manuscript (Lines 174-179).

Point 7: Results and discussion part is weak, not informative, should be inline of the results presented in tables and figures

We have tried to improve the quality of our write-up. Results are now discussed in detail (In Red color).

Point 8: In Conclusion “The result shows that the lack of adaptive capacity and readiness in lower and upper-middle-income countries severely impedes the pursuit of low-carbon development, excluding higher-income countries”, Where it was incited in the results and Discussion of the section of the paper.

We have made the corrections in the entire results and discussion section. We have gone through our manuscript carefully and made changes for improvement (Lines: 557-560).

Point 9: Survey was conducted by the authors; it is based on some other studies.

Dear Reviewer, From point 9, up to my understanding, you are asking about the data employed in the underlying study. The data for the study were extracted from ND-Gain 2015 and 2022. (Reference Nos. 67 and 68)

Point 10: Others climax the high sensitivity to climate hazards due to the dependence of these countries on climate-sensitive sectors, such as agriculture; these two factors matter (Bowen et al., 2012; Fankhauser, 2017)., what are two factors?????

These two factors are high exposure and high sensitivity (Lines 312-314).

Developing countries are more vulnerable to climate change, High vulnerability is defined as a situation where the exposure to climate risks is high and the sensitivity of the system is high.

Point 11: What are predicted and predictors variable

The predicted/dependent variable is the adaptive capacity (ADC) and predictors/explanatory variables are adaptation readiness (RED), climate change (EF), economic development (GDP), foreign direct investment (FDI), renewable energy (RE), and natural capital (NC).

Moreover, lines 222-232 discuss variables in the manuscript.

Point 12: On What bias these test and hypotheses was selected for the study.

This study selected the hypothesis on the basis of a theory/theoretical framework. As we have employed the panel data, the error term ( ) may experience econometric issues like cross-sectional dependence, autocorrelation, and heteroscedasticity in the panels Therefore, we applied several diagnostic tests.

We have tried to explain it in section 4.1. Pre-estimation diagnostic tests

We appreciate the time and efforts of the editor and reviewers in reviewing the manuscript. We have tried our best to address all issues indicated by the reviewer and believed that the revised version will meet the publication requirements.

REVIEWER # 2

Reviewers comments

Author(s) response*

Point 1: Tables and figures have irregularities and should be adjusted according to the format of the journal. For example, there are 2 Figure.1 and Figure 5 has shifted.

We have made the correction. Now the figures are shifted and adjusted according to the format of the journal.

Point 2: Figure and Table cited in the text should be corrected in accordance with the journal format.

We have tried our best to take into account your suggestions. Now all the tables and figures are presented in accordance with the format of the journal

Point 3: The link attributed to the figures goes to the chart titled "Global Tourism Index (1995-2019)".

We have made the corrections in the entire manuscript.

Point 4: In paragraph 3.3.4 FDI Inflow, FDI is defined three times. It is also undefined in the introduction and abstract sections. However, the abbreviation should be defined in the first appropriate place, and then it is better to use the abbreviation.

We have tried to follow your instructions and taken into account all comments. We have revised the entire section 3.3.4. FDI Inflow and also added it to the abstract.

Point 5:  In the paragraph titled "4.1.2. Slope Heterogeneity test" T and N have not been defined before. The definition of these and other abbreviations should also be checked.

We have tried our best to define each abbreviation. We have gone through our manuscript carefully and made changes.

Now the T and N are defined in section 4.1. Pre-estimation diagnostic tests

Point 6: In the abstract and conclusion, countries with a low value for the adaptation readiness index according to different income groups can be given.

We have revised these sections by adding the results.