Energy Transition and Poverty Alleviation in Light of Environmental and Economic Challenges: A Comparative Study in China and the European Union Region

Review Report 1

Comments

Replies

1- Clarity and Structure: The introduction could be more engaging and clearly structured. Introduce the main research question earlier and outline the paper's structure.

we have tried to ‎ more clarify the structure of the paper as well as the main research question. All modification are colored in yellow color as bellow: This essay explores the complex interrelationships between environmental and economic ‎variables that influence global trends. The main areas of interest for us are the relationships ‎between poverty, the use of renewable energy sources like wind and solar power, the adoption ‎of a circular economy that reduces waste and fossil fuel consumption, economic growth ‎‎(measured by GDP), and trade openness . The following is, in essence, the primary research ‎question: Is it possible to promote the shift to renewable energy sources, a circular economy, ‎and more trade openness while also reducing CO2 emissions and achieving sustainable ‎development, which is defined by economic growth, poverty reduction, and environmental ‎responsibility?‎

In actuality, the paper's structure is as follows: The research techniques used in the study are ‎described in the Methodology and Data section. We will go over the procedures for gathering ‎the data, the particular variables that were examined (CO2 emissions, rates of poverty, the ‎capacity of solar and wind power, indicators of the circular economy, the consumption of ‎fossil fuels, GDP, and trade openness data), and the econometric methods that were ‎employed to look at the correlations between these variables.‎

The main study findings are presented in the Results and Discussion section. We will go over ‎the statistical connections between trade openness, GDP, poverty, renewable energy sources, ‎the circular economy, and CO2 emissions. We will investigate the interplay between these ‎variables and assess the possibility of achieving sustainable development by means of focused ‎policy measures. Finally, a summary of the key findings from the study is provided in the ‎section on Conclusion and Policy Implications. We will discuss the policy implications of our ‎research and offer possible plans for utilizing renewable energy, a circular economy, and more ‎trade openness to foster economic development, fight poverty, and reduce CO2 emissions. ‎We'll wrap off by highlighting the necessity of an all-encompassing strategy that tackles these ‎interrelated problems holistically.‎

2- Literature Review: Expand the literature review to include more recent studies, ensuring a comprehensive understanding of the current research landscape.

Wu, J., Yang, C., Chen, L. (2024). Examining the non-linear effects of monetary policy on carbon emissions.

Energy Economics

. 107206

Cao, Q., Feng, Z., Yang, R., Yang, C. (2024). Conflict and natural resource condition: An examination based on national power heterogeneity.

Resources Policy

. 89, 104549

We have tried to include more recent studies in Literature Review.

All modifications are colored in yellow color as bellow:

Wu, J. et al. (2024) have studied how monetary policy affects carbon emissions in non-linear ‎ways. The authors notably focused on non-linear effects in their investigation of the link ‎between monetary policy and carbon emissions. The research was published in the Energy ‎Economics journal. Wu, Yang, and Chen's study from 2024 adds to the expanding corpus of ‎research on how monetary policy affects environmental consequences. The authors ‎investigated the intricate connection between monetary policy and carbon emissions using ‎non-linear models. This strategy is crucial because it enables a more sophisticated ‎understanding of how monetary policy affects environmental sustainability. Based on national ‎power heterogeneity, Cao, Q et al. (2024) studied the link between conflict and the state of ‎natural resources. The effect of differences in national power on the state of natural resources ‎was investigated by the writers. The knowledge of how power dynamics shape the state of ‎natural resources is aided by this study. The results of this study may affect how resources are ‎managed and policies are made.‎

3- Methodology: Provide a more detailed explanation of the ARDL approach and VECM technique. Clarify why these methods are suitable for this study.

We have tried to provide a more detailed explanation of the ARDL approach and VECM technique.

All modifications are colored in yellow color as bellow:

 The ARDL methodology and the VECM technique are two econometric methods for analyzing ‎time series data. Because of their unique advantages, these methods may be used to investigate ‎the connections between trade openness, GDP, poverty, renewable energy, the circular economy, ‎and the use of fossil fuels. The ARDL technique is essentially a versatile method for cointegration ‎analysis. First of all, it works well with mixed integration; in fact, it performs best with variables ‎that may have a variety of integration orders, some of which may be stationary (constant variance ‎over time) at level (I(0)) or after one differentiating (I(1)). This is perfect for your research ‎because poverty, economic variables (GDP, trade openness), and the use of fossil fuels or ‎renewable energy sources may both be I(0) or I(1).‎ ‎

Second, the ARDL method is very helpful when dealing with sparse data, which is a typical ‎problem in economic and environmental studies. This is due to the fact that it is effective with ‎smaller datasets as it integrates the estimation of long-run cointegration and short-run dynamics in ‎a single step. Finally, limits testing a statistical method to ascertain if a cointegrating relationship ‎between the variables exists is made possible by the ARDL. This makes it easier to determine if ‎these elements are in long-term balance. Nonetheless, the VECM method makes the assumption ‎that the variables are already cointegrated. This might not be the best option for our study at first ‎since, before employing VECM, we need to verify cointegration using tests like Johansen's ‎cointegration test.‎ Emphasis on short-run dynamics: VECM is particularly good at examining the ‎short-run dynamics between the variables after cointegration has been achieved. It illustrates the ‎process by which temporal deviations from the long-run equilibrium represented by the ‎cointegrating equation are adjusted. This is useful in understanding the potential short-term ‎effects on poverty or GDP of changes in trade openness or the uptake of renewable energy. Error ‎Correction Mechanism (ECM) term included in VECM provides flexibility by explaining the pace ‎and amount at which the variables return to their long-term equilibrium connection. This offers ‎insightful information on the system's capacity for self-correction.‎ In conclusion, ARDL and ‎VECM may be applied in turn to thoroughly examine the relationships in your study: To begin, ‎use ARDL: To check for cointegration among the variables, use ARDL. If there is cointegration, ‎move forward with VECM: Use VECM to learn more about the short-run dynamics and how ‎these variables move towards their long-run equilibrium if ARDL verifies cointegration. ‎Combining these techniques gives you a comprehensive grasp of this complicated system's short-‎term modifications as well as its long-term stable linkages.‎ In conclusion, ARDL's flexibility with ‎mixed integration and small samples makes it a great starting point. If cointegration is confirmed, ‎VECM provides valuable insights into the short-run dynamics, allowing you to paint a ‎comprehensive picture of how poverty, renewable energy, the circular economy, fossil fuel ‎consumption, GDP, and trade openness interact with each other .‎

4- Data Sources and Justification: Clearly state the reasons for choosing the specific data sources. If possible, include data from more diverse sources to strengthen the study's reliability.

The selection of data sources is paramount for robust research. In this study, we leverage data from three credible sources to ensure the reliability and generalizability of our findings:

World Development Indicators (WDI) database (2023):

Justification: The WDI, compiled by the World Bank, is a globally recognized repository for development-related data. It offers a vast array of indicators across numerous countries, making it ideal for cross-country comparisons. In your study, it likely provides data on poverty, GDP, and trade openness for a broad range of countries.

Eurostat-European Commission (2023):

Justification: Eurostat serves as the official statistical office of the European Union. It offers in-depth, high-quality data specifically focused on European member states. This source might be particularly valuable if your study includes a significant focus on European economies or aims to compare them with others.

National Bureau of Statistics of China (2023):

Justification: China's National Bureau of Statistics is the primary source for official Chinese data. Including data from China is crucial for capturing the dynamics of a major global economy, especially considering its significant role in renewable energy production and manufacturing.

5- Comparative Analysis: Deepen the comparative analysis between China and the EU. Discuss more about how their different socio-economic and environmental contexts influence the study's outcomes

You found the new modifications in Methodology and data section colored in yellow color as bellow:

In effect, China's rising economy places equal emphasis on environmental sustainability and ‎growth, which might result in an increase in renewable energy sources in addition to economic ‎growth. The EU may be able to discern a stronger connection between poverty reduction and ‎decreasing reliance on fossil fuels due to its emphasis on social assistance and regulations. ‎China's energy balance is changing as the country relies less on coal and more on renewable ‎energy sources. This may indicate a complicated short-term link between emissions and fossil ‎fuels. With its well-established renewable industry, the EU may show a more pronounced ‎inverse relationship between CO2 emissions and renewable energy. In China, the circular ‎economy generates jobs in waste management, which may indicate a connection between its ‎acceptance and the decline of poverty. Leading the way in circular practices, the EU may ‎demonstrate a tighter connection between resources recovery and lower fossil fuel ‎consumption. ‎ China's trade openness has a complicated connection with pollution. While ‎greater production volumes may cause emissions to rise, increased efficiency may help reduce ‎them. The EU may see a stronger correlation between commerce and sustainability due to its ‎emphasis on environmental requirements in trade agreements. Different methods to policy are ‎used. The centralized government of China makes it possible to enact environmental laws ‎swiftly, which accelerates the reduction of energy use. The EU's emphasis on rules may cause ‎the shift to a low-carbon economy to happen more slowly but maybe more sustainably.‎

6- Graphs and Tables: Improve the clarity and presentation of graphs and tables. Ensure they are well-labeled and effectively support the narrative.

We will be revising the graphs and tables to ensure they are well-labeled and effectively support the narrative of the paper.

7- Discussion of Findings: Elaborate on the implications of your findings in a broader context. Discuss how they contribute to the existing body of knowledge and their practical implications.

You found the new modifications in long run ARDL estimation section (before Table5) colored in yellow color as bellow:

After approval the order of stationarity of variables and confirmation of the existence of cointegration using Bounds test results, we can now estimate the long and short-run coefficients. The different results of ARDL long-run coefficients are situated in Table 5.

The results of the study indicate that poverty (POV), fossil consumption (FC), gross domestic product (GDP), and trade openness (TO) are significant factors contributing to increased CO2 emissions in both China and the European Union (EU). However, the impact of these factors differs significantly between the two regions. In China, poverty, fossil consumption, GDP, and TO are found to increase CO2 emissions by 4.153, 15.893, 5.979, and 3.361, respectively, which are much higher than the impact of these factors on CO2 emissions in the EU. In the EU, poverty, fossil consumption, GDP, and TO increase CO2 emissions by 0.546, 1.011, 2.939, and 0.033, respectively. These findings suggest that the impact of POV, FC, GDP, and TO on CO2 emissions is more significant in China than in the EU. This highlights the need for region-specific strategies to tackle carbon emissions, considering the unique socio-economic and environmental factors that drive carbon emissions in each region.

The positive correlation between GDP and CO2 emissions confirms the existing knowledge that economic development often comes at the cost of environmental degradation, especially in the early stages of industrialization. This highlights the need for sustainable development strategies that prioritize clean energy and resource efficiency. The impact of trade openness (TO) on CO2 emissions is interesting. It suggests that increased trade can lead to higher emissions, possibly due to the movement of production processes to countries with less stringent environmental regulations. This challenges the idea that globalization inherently reduces emissions and calls for international cooperation on environmental standards. The finding that poverty contributes to CO2 emissions sheds light on a critical challenge. Low-income populations often rely on cheap, polluting energy sources like coal for basic needs. This creates a trap where poverty hinders investment in clean energy, perpetuating reliance on fossil fuels and hindering development.

The results suggest also, that in China, the implementation of wind power, solar power, and circular economy measures have been successful in reducing CO2 emissions. Specifically, wind power decreased CO2 emissions by 3.197, solar power decreased CO2 emissions by 4.181, and circular economy measures decreased CO2 emissions by 3.1. In comparison, the European Union (EU) has not seen the same level of success in reducing CO2 emissions through these measures. Wind power decreased CO2 emissions by 2.939, solar power only decreased CO2 emissions by 0.013, and circular economy measures decreased CO2 emissions by 0.788. This reinforces the existing body of knowledge that renewable energy sources like wind and solar are effective alternatives to fossil fuels. Their expansion displaces polluting sources, leading to a cleaner energy mix and demonstrably lower CO2 emissions. Also, this finding adds weight to the emerging understanding of the circular economy's role in climate change mitigation. By reducing reliance on virgin materials through recycling, reuse, and repair, the circular economy lowers emissions associated with resource extraction and production processes.

These results suggest that there may be differences in the implementation or effectiveness of these measures between China and the EU, highlighting the need for further research and analysis to better understand these differences and identify ways to improve the effectiveness of these measures.

The results show that the impact of solar power, circular economy, fossil consumption, and CO2 emissions on poverty reduction varies significantly between China and the European Union (EU). In China, solar power had the highest impact on poverty reduction with a decrease of 41.317 in poverty rate, followed by circular economy measures with a decrease of 3.035 in poverty rate. Fossil consumption and CO2 emissions also have significant impacts on poverty reduction with decreases of 1.715 and 0.959, respectively. However, wind power, gross domestic product, and trade openness had a slight negative impact on poverty reduction in China. In comparison, in the EU, CO2 emissions and fossil consumption have a positive impact on poverty rate, increasing it by 0.035 and 4.979, respectively. On the other hand, wind power, solar power, circular economy measures, gross domestic product, and trade openness had a positive impact on poverty reduction in the EU, with circular economy measures having the highest impact with a decrease of 0.128 in poverty rate. These results highlight the need for region-specific policies and measures to tackle poverty and reduce the impacts of climate change. Renewable energy and circular economy play a vital role in poverty reduction. Solar power in China and circular economy measures in the EU demonstrate that focusing on sustainability can have a positive social impact.

The negative impact (of fossil fuels and CO2 emissions) in China and positive association in the EU highlight the complexities at play. In developing economies, fossil fuels might be crucial for initial economic growth, lifting some out of poverty. However, in developed regions, their long-term environmental consequences could worsen poverty through climate change impacts.

8- Conclusion and Policy Recommendations: Provide a more robust conclusion that summarizes key findings. Offer specific, actionable policy recommendations based on the study's results.

We have added some modifications in Conclusion and Policy implication section colored in yellow color and we have added a new section ‘’ ‎6. Key Findings’’

9- Additionally, paying attention to grammar and consistency in formatting throughout the paper will enhance its overall quality.

Thank you for your feedback. we are agree that ensuring proper grammar and consistent formatting is crucial for a high-quality paper. We will carefully proofread the manuscript again and address any identified inconsistencies before submitting the revised version.

Review Report 2

Comments

Replies

1- A quantitative study was carried out, which has little connection with the existing theory and literature analysis. The authors need to reorganize their introduction and create a separate section dedicated to literature analysis, somewhat deeper than now because now the topic is analyzed without taking into account such fundamental concepts as the definition of European poverty. More on this can be found here: Dvorak, J. (2015).European Union definition of poverty. The SAGE Encyclopedia of world poverty, 2, 528-529.

We have reorganized the introduction and we have added a new section (2. Literature review).

Also we have added the definition of European poverty (Dvorak, J. (2015).European Union definition of poverty. The SAGE Encyclopedia of world poverty, 2, 528-529)

All modifications are colored in blue color.

2- there is a brief presentation of information about climate change, the circular economy in the introduction, but there is no more serious literature review, and in the EU-27 it is a different story and this can be useful here: Kotseva-Tikova, M., & Dvorak,J. (2022 ). Climate Policy and Plans for Recovery in Bulgaria and Lithuania. Romanian Journal of European Affairs, 22(2).

We have added a new subsection in Literature review (Climate change and circular economy).

All modifications are colored in blue color.

Review Report 3

Comments

Replies

1- The abstract should clearly articulate the study's main objective, scope, and significance. It should be free of jargon and accessible to a broad audience. The abstract should highlight the study's principal findings, emphasizing the novel insights gained from the comparative analysis between China and the EU. It should briefly discuss the implications of the findings for policy, practice, and future research, particularly concerning energy transition and poverty alleviation.

We have rewrite the abstract and we have tried to include all the comments you requested.

All modifications are colored in green color.

2- The introduction should more precisely define the scope and objectives of the study. It currently provides a broad context but lacks specificity regarding the unique aspects of the comparative analysis between China and the EU. The introduction lacks clear research questions. Articulating these would provide a clearer direction for the study and help readers understand the expected contributions.

We have rewrite the introduction and we have tried to include all the comments you requested.

3- Questions about the originality and contribution of the study to the existing body of knowledge, particularly the findings do not significantly advance understanding or if the study closely replicates previous work without sufficient novelty.

We have tried to clarify the originality and contribution of the study at ‘’Introduction’’ section as bellow:

The study's originality lies in its comparative analysis of China and the EU. While research exists on renewable energy, circular economy, and their impact on poverty and emissions, focusing on these factors in both regions and comparing their effectiveness is a fresh contribution. The research combines analysis of both environmental damage (CO2 emissions) and poverty reduction, offering a more comprehensive picture of the impact of renewable energy and circular economy practices. By highlighting the different effectiveness of these strategies in China vs. EU, the study provides valuable insights for policymakers in each region. This knowledge can inform the development of more targeted and successful policies for tackling poverty and climate change

4- I identified the shortcomings in the literature review, such as overlooked key studies, an imbalance in the representation of different perspectives, or a lack of critical engagement with the literature.

We have added a new section ‘’2. Literature review’’ and we have tried to include all the comments you requested.

5- The theoretical framework should explain the weaknesses or gaps in the theoretical underpinnings of the research. It could involve the conceptual framework, hypotheses, or the linkage between theory and empirical analysis.

We have tried to explain the gaps at Introduction section (colored in green color) as bellow:

The research design focuses on examining the potential of renewable energy and circular ‎economy for reducing environmental damage and poverty. It try to explicitly address the ‎direction of causality. For example, does reducing environmental damage necessarily lead to ‎poverty reduction, or vice versa? Are there other factors at play. Also, the research mentions ‎using ARDL and VECM techniques, which are good for analyzing relationships between ‎variables. However, the research looks at China and the EU as a whole. though, both regions ‎have significant internal economic and development variations. The impact of renewable ‎energy and circular economy policies might differ across provinces in China or member states ‎in the EU. ‎

6- In Methodology, the robustness and appropriateness of the ARDL approach and VECM technique are questioned, as well as their application and the justification for choosing these methods over others.

the justifications for choosing the ARDL approach and VECM technique (colored in yellow color at Introduction section) are as bellow:

The ARDL methodology and the VECM technique are two econometric methods for analyzing time series data. Because of their unique advantages, these methods may be used to investigate the connections between trade openness, GDP, poverty, renewable energy, the circular economy, and the use of fossil fuels. The ARDL technique is essentially a versatile method for cointegration analysis. First of all, it works well with mixed integration; in fact, it performs best with variables that may have a variety of integration orders, some of which may be stationary (constant variance over time) at level (I(0)) or after one differentiating (I(1)). This is perfect for your research because poverty, economic variables (GDP, trade openness), and the use of fossil fuels or renewable energy sources may both be I(0) or I(1).‎

Second, the ARDL method is very helpful when dealing with sparse data, which is a typical problem in economic and environmental studies. This is due to the fact that it is effective with smaller datasets as it integrates the estimation of long-run cointegration and short-run dynamics in a single step. Finally, limits testing a statistical method to ascertain if a cointegrating relationship between the variables exists is made possible by the ARDL. This makes it easier to determine if these elements are in long-term balance. Nonetheless, the VECM method makes the assumption that the variables are already cointegrated. This might not be the best option for our study at first since, before employing VECM, we need to verify cointegration using tests like Johansen's cointegration test.‎ Emphasis on short-run dynamics: VECM is particularly good at examining the short-run dynamics between the variables after cointegration has been achieved. It illustrates the process by which temporal deviations from the long-run equilibrium represented by the cointegrating equation are adjusted. This is useful in understanding the potential short-term effects on poverty or GDP of changes in trade openness or the uptake of renewable energy. Error Correction Mechanism (ECM) term included in VECM provides flexibility by explaining the pace and amount at which the variables return to their long-term equilibrium connection. This offers insightful information on the system's capacity for self-correction.‎ In conclusion, ARDL and VECM may be applied in turn to thoroughly examine the relationships in your study: To begin, use ARDL: To check for cointegration among the variables, use ARDL. If there is cointegration, move forward with VECM: Use VECM to learn more about the short-run dynamics and how these variables move towards their long-run equilibrium if ARDL verifies cointegration. Combining these techniques gives you a comprehensive grasp of this complicated system's short-term modifications as well as its long-term stable linkages.‎ In conclusion, ARDL's flexibility with mixed integration and small samples makes it a great starting point. If cointegration is confirmed, VECM provides valuable insights into the short-run dynamics, allowing you to paint a comprehensive picture of how poverty, renewable energy, the circular economy, fossil fuel consumption, GDP, and trade openness interact with each other .‎

7- The discussion section overstates the findings' implications, draws conclusions not supported by data, or fails to acknowledge limitations and alternative explanations.

You found the new modifications in long run ARDL estimation section (before Table5) colored in yellow color as bellow:

After approval the order of stationarity of variables and confirmation of the existence of cointegration using Bounds test results, we can now estimate the long and short-run coefficients. The different results of ARDL long-run coefficients are situated in Table 5.

The results of the study indicate that poverty (POV), fossil consumption (FC), gross domestic product (GDP), and trade openness (TO) are significant factors contributing to increased CO2 emissions in both China and the European Union (EU). However, the impact of these factors differs significantly between the two regions. In China, poverty, fossil consumption, GDP, and TO are found to increase CO2 emissions by 4.153, 15.893, 5.979, and 3.361, respectively, which are much higher than the impact of these factors on CO2 emissions in the EU. In the EU, poverty, fossil consumption, GDP, and TO increase CO2 emissions by 0.546, 1.011, 2.939, and 0.033, respectively. These findings suggest that the impact of POV, FC, GDP, and TO on CO2 emissions is more significant in China than in the EU. This highlights the need for region-specific strategies to tackle carbon emissions, considering the unique socio-economic and environmental factors that drive carbon emissions in each region.

The positive correlation between GDP and CO2 emissions confirms the existing knowledge that economic development often comes at the cost of environmental degradation, especially in the early stages of industrialization. This highlights the need for sustainable development strategies that prioritize clean energy and resource efficiency. The impact of trade openness (TO) on CO2 emissions is interesting. It suggests that increased trade can lead to higher emissions, possibly due to the movement of production processes to countries with less stringent environmental regulations. This challenges the idea that globalization inherently reduces emissions and calls for international cooperation on environmental standards. The finding that poverty contributes to CO2 emissions sheds light on a critical challenge. Low-income populations often rely on cheap, polluting energy sources like coal for basic needs. This creates a trap where poverty hinders investment in clean energy, perpetuating reliance on fossil fuels and hindering development.

The results suggest also, that in China, the implementation of wind power, solar power, and circular economy measures have been successful in reducing CO2 emissions. Specifically, wind power decreased CO2 emissions by 3.197, solar power decreased CO2 emissions by 4.181, and circular economy measures decreased CO2 emissions by 3.1. In comparison, the European Union (EU) has not seen the same level of success in reducing CO2 emissions through these measures. Wind power decreased CO2 emissions by 2.939, solar power only decreased CO2 emissions by 0.013, and circular economy measures decreased CO2 emissions by 0.788. This reinforces the existing body of knowledge that renewable energy sources like wind and solar are effective alternatives to fossil fuels. Their expansion displaces polluting sources, leading to a cleaner energy mix and demonstrably lower CO2 emissions. Also, this finding adds weight to the emerging understanding of the circular economy's role in climate change mitigation. By reducing reliance on virgin materials through recycling, reuse, and repair, the circular economy lowers emissions associated with resource extraction and production processes.

These results suggest that there may be differences in the implementation or effectiveness of these measures between China and the EU, highlighting the need for further research and analysis to better understand these differences and identify ways to improve the effectiveness of these measures.

The results show that the impact of solar power, circular economy, fossil consumption, and CO2 emissions on poverty reduction varies significantly between China and the European Union (EU). In China, solar power had the highest impact on poverty reduction with a decrease of 41.317 in poverty rate, followed by circular economy measures with a decrease of 3.035 in poverty rate. Fossil consumption and CO2 emissions also have significant impacts on poverty reduction with decreases of 1.715 and 0.959, respectively. However, wind power, gross domestic product, and trade openness had a slight negative impact on poverty reduction in China. In comparison, in the EU, CO2 emissions and fossil consumption have a positive impact on poverty rate, increasing it by 0.035 and 4.979, respectively. On the other hand, wind power, solar power, circular economy measures, gross domestic product, and trade openness had a positive impact on poverty reduction in the EU, with circular economy measures having the highest impact with a decrease of 0.128 in poverty rate. These results highlight the need for region-specific policies and measures to tackle poverty and reduce the impacts of climate change. Renewable energy and circular economy play a vital role in poverty reduction. Solar power in China and circular economy measures in the EU demonstrate that focusing on sustainability can have a positive social impact.

The negative impact (of fossil fuels and CO2 emissions) in China and positive association in the EU highlight the complexities at play. In developing economies, fossil fuels might be crucial for initial economic growth, lifting some out of poverty. However, in developed regions, their long-term environmental consequences could worsen poverty through climate change impacts.

8- The policy recommendations are not sufficiently grounded in the study's findings, are too broad, or do not consider practical implementation challenges; this could be a point of critique.

We have added some modifications in ‎Conclusion and Policy implication section ‎colored in yellow color and we have added a ‎new section ‘’‎‏ ‏‎6. Key Findings’’‎

9- The broader impacts of the research, especially if the study's relevance to current challenges in energy transition and poverty alleviation, are not convincingly demonstrated.

At the end of ‘’Conclusion and Policy implication’’ section, we have tried to answer to your comment as bellow:

The research emphasizes the importance of understanding regional variations. This knowledge is crucial for crafting effective policies that tackle poverty and address environmental vulnerability specific to each region (China vs. EU). It highlights the need for knowledge sharing platforms and global cooperation. By encouraging both regions to learn from each other's experiences and develop best practices, the research promotes collaboration that can accelerate progress in tackling poverty and climate change.

In term of limitations, the research doesn't explicitly explore the mechanisms through which renewable energy and circular economy reduce poverty. Future research could delve deeper into these mechanisms to provide a more comprehensive understanding of the causal relationships. The timeframe (1995-2022) might not fully capture the long-term impact on poverty reduction. Further research with a longer timeframe could strengthen the connection between these factors.Haut du formulaire

Review Report 4

Comments

Replies

1- what the finding of the paper is and  how this benefits the area of sustainability research.

For examples: 1) The literature review is a run-on paragraph that jumps from finding to finding without ever tying the papers together in a coherent means to establish expectations or hypotheses.

- We have added a new section ‘’ ‎6. Key Findings‎’’ to discus finding of the paper.

- We have added a new section ‘’Literature review’’.

2- Terms like "Poverty" and "Circular Economy", key to the paper, are never defined nor given context nor are their measurements defined and units given. For example, Line 329 states "with a decrease of 41.317 in poverty rate" but the units of poverty are never defined. Is this saying there is a 41% decline in the poverty rate? i.e. Poverty was 80% and now its 33%? or is it saying a decline of 41%? Or is it even showing a rate decline at all? I have no idea because units are not defined.

The numbers are in percentage and we have added a note under Table5: Long-run ARDL estimates which indicates that all numbers are in percentage.

Review Report 5

Comments

Replies

1- Avoid repeating the same terminology in the same paragraph

more than five times (policymakers in the abstract section).

We have tried to rewrite the Abstract section

2- More clarifications of different terminologies must be addressed.

We have tried to explain and clarify the different terminologies.

3- The clarification of the diagrams and abbreviations is a must.

We have tried to explain and clarify the diagrams and abbreviations like for example: ARDL, VECM, VAR, GDP, PP, ADF, EU, LM, ARCH, JB, CUSUM, CUSUMSQ.etc.

4- Authors have to clarify the connection between these variables

(Poverty, Renewable energy, Solar power, Carbon dioxide, etc.).

We have added a new section ‘’2. Literature review’’ to study the connection between variables

5- The Results and Discussions could be improved.

We have tried to more improve the Results and Discussion.

6- The number of references is low for a journal paper.

We have added new references

Review Report 3

Abstract

Comments

Replies

1.     Clearly state the study's primary objective to provide a more precise direction and focus.

We have tried to more clarify the study's primary objective to provide a more precise direction and focus.

The new modifications are colored by green color as bellow:’’ The study aims to compare the effectiveness of renewable energy and circular economy practices in reducing environmental damage and poverty in China and the European Union (EU) by using the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) technique.’’

2.     Detail the novel insights from the comparative analysis, emphasizing what sets this study apart from existing literature.

We have tried to give more detail for the novel insights from the comparative analysis as bellow (colored in green color):

‘’ This study offers several fresh perspectives on the fight against environmental damage and poverty, differentiating itself from existing research in a few key ways. In fact, most studies focus on a single region's approach to sustainability. However, this research directly compares China and the European Union (EU), highlighting how factors like economic structure and development stages influence the effectiveness of renewable energy and circular economy practices. Also, the study shows how renewable energy and circular practices can simultaneously tackle both environmental damage (emissions) and social issues (poverty).

3.     Briefly outline the implications of the findings for policy, practice, and future research in a manner that conveys their significance and potential impact.

The new modifications are as bellow (colored in green color):

‘’ The study suggests a one-size-fits-all approach won't work for fighting climate change and poverty. While renewable energy and circular economies are effective, China saw greater success with solar and wind power compared to the EU. Policymakers need to design strategies specific to their region's economic, social, and environmental context. Further research can refine these approaches, and public education is crucial to promote sustainable behavior and empower policymakers’’

Introduction

4.     Define the scope and objectives more precisely to guide the reader through the study's unique aspects and contributions.

We have tried to more define the scope and objective more precisely.

The new modifications are colored in green color as bellow:

‘’ To sum up, this study focuses on the effectiveness of renewable energy and circular ‎economies in tackling two major challenges: poverty and environmental degradation. The ‎unique aspect lies in comparing these strategies in China and the EU, two significant global ‎energy consumers. Previous research has explored these themes, but this study is the first to ‎directly compare their effectiveness across these regions. By employing ARDL and VECM ‎analysis, the study goes beyond simply identifying correlations and aims to determine the ‎direction of causality between these factors. While recognizing the internal economic ‎variations within each region, the study offers valuable insights for policymakers nonetheless, ‎allowing them to develop more targeted strategies for their specific contexts. ‎’’

5.     Formulate clear research questions to provide a focused narrative and outline the study's expected contributions.

The new research questions are as bellow (colored in green color):

How do economic structure and development stage influence the effectiveness of renewable energy and circular economy practices in reducing environmental damage and poverty in China compared to the EU? Can renewable energy and circular economies be implemented to simultaneously address environmental damage (CO2 emissions) and social issues (poverty) in both China and the EU?

6. Strengthen the motivation behind the study by articulating the significance of the comparative analysis between China and the EU in the context of energy transition and poverty alleviation.

The new modifications are colored in green color as bellow:

‘’While existing research explores renewable energy, circular economies, and their impact on poverty and emissions, it often focuses on single regions. This study explains a crucial gap exists - understanding how these strategies work across different contexts. China and the EU, as major global energy consumers with distinct economic structures and development stages, present a unique opportunity for comparison. By analyzing both regions, the study sheds light on how these factors influence the effectiveness of sustainability efforts. This comparative approach is significant because it allows us to move beyond a one-size-fits-all mentality. By seeing how China and the EU achieved varying success with similar strategies, policymakers gain valuable insights. This knowledge empowers them to design targeted approaches that consider their region's specific social, economic, and environmental landscape, ultimately leading to a more effective fight against climate change and poverty.’’

Literature Review

7. Engage more critically with the existing literature by summarizing and evaluating key studies and their findings, methodologies, and limitations.

All new modifications are colored in yellow color

8. Ensure a balanced representation of different perspectives to provide a comprehensive overview of the research area.

All new modifications are colored in blue color

9. Highlight the gaps or inconsistencies in the literature your study aims to address, reinforcing the study's originality and contribution.

10. Elucidate the research's theoretical framework, including any conceptual models, theories, or hypotheses guiding the analysis.

11. Explain how the theoretical framework informs the research design, methodology, and interpretation of findings, enhancing the study's coherence and depth.

We have tried to answer to these comments in form of paragraph colored in red color as bellow:

‘’ Our research tackles shortcomings in our understanding of renewable energy, circular economy, and their impacts on poverty and CO2 emissions. Existing studies often overlook significant regional variations, focusing on general trends. This research addresses this gap by using a comparative approach between China and the EU, highlighting the need for region-specific policies. Additionally, while prior research acknowledges the potential of renewable energy and circular economy practices, the degree of effectiveness remains unclear. This study employs ARDL models to quantify the impact of these practices on both CO2 emissions and poverty reduction in each region. Furthermore, existing research primarily focuses on individual countries or regions. This research emphasizes the importance of knowledge sharing between China and the EU to accelerate progress in tackling shared sustainability challenges. The theoretical framework of sustainable development, recognizing the interconnectedness of environmental and socio-economic factors, guides this research. This framework informs the methodology in several ways. The Bounds test and ARDL models identify long-run relationships between variables, aligning with the theory of lasting connections between poverty, CO2 emissions, and sustainability practices. Additionally, the VECM explores the complex causal relationships between these variables, acknowledging the multifaceted nature of their interactions. Finally, the comparative approach reflects the understanding that successful sustainability strategies require considering the specific contexts of each region.

By employing a methodology grounded in these principles, the research offers a more nuanced understanding of how renewable energy and circular economy practices contribute to poverty reduction and CO2 emission mitigation in China and the EU. This enriches the existing body of knowledge by highlighting the importance of regional variations and global collaboration in achieving sustainability goals.’’

Methodology

12. Provide a more detailed justification for selecting the ARDL and VECM approaches, discussing their suitability for the study's objectives and context.

The new modifications are added in ‘’Methodology and data’’ section colored in yellow color as bellow:

‘’ Both ARDL and VECM are well-suited to analyze the impact of renewable energy and circular economy practices on environmental damage (emissions) and poverty reduction in two distinct regions (China and EU) over a time period (1995-2022). These models can capture both the short-term effects (how changes in one variable immediately impact another) and the long-term equilibrium relationships between the variables. The study likely involves analyzing several variables like poverty, fossil fuel reliance, economic growth, international trade, renewable energy adoption, and circular economy measures. ARDL and VECM are powerful tools for handling such complex scenarios where multiple variables potentially influence each other. However, a key aspect of the research is comparing the effectiveness of these sustainability practices in China and the EU. These models allow for the investigation of how these variables interact differently in each region, considering factors like economic structure and development stages. In essence, ARDL and VECM provide a robust framework to analyze the interconnectedness of these variables over time, compare the effectiveness of different approaches across regions, and understand the complex dynamics at play in the fight against environmental damage and poverty.’’

13.  Clarify the criteria for selecting variables and their operationalization to ensure they accurately represent the constructs of interest.

All justifications are colored ib blue color as bellow:

‘’ In selecting variables to analyze the role of renewable energy and the circular economy in saving the environment and reducing poverty, several criteria were considered to ensure the accurate representation of the constructs of interest. Firstly, CO2 emissions were chosen as a key indicator of environmental impact, reflecting the extent of greenhouse gas emissions and their contribution to climate change. Poverty, as a multidimensional concept, was operationalized using indicators such as income levels, access to basic services, and living standards, aiming to capture the socioeconomic dimensions of well-being. Renewable energy sources, specifically solar power and wind power, were included to assess their potential in reducing reliance on fossil fuels and mitigating environmental degradation. The circular economy, characterized by principles of resource efficiency and waste reduction, was selected to evaluate its role in sustainable development and resource conservation. Fossil fuel consumption was included as a contrasting variable to renewable energy, reflecting the dependence on non-renewable resources and their environmental implications. Per capita gross domestic product (GDP) served as a measure of economic development and prosperity, while trade openness reflected the degree of integration into the global economy and its implications for environmental and social outcomes. By considering these variables, the analysis aimed to provide a comprehensive understanding of the interplay between renewable energy, the circular economy, environmental sustainability, poverty reduction, and economic development in the contexts of China and the European Union.’’

14.  Discuss the assumptions, limitations, and potential biases of the chosen methods, as well as strategies employed to address them.

All justifications are colored in red color as bellow:

‘’ While the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction ‎Model (VECM) technique offer valuable insights into the dynamics between renewable ‎energy, circular economy practices, environmental damage, and poverty, they also come with ‎certain limitations. One limitation of the ARDL approach is its sensitivity to model ‎specification. The choice of lag length and included variables can significantly impact the ‎results, potentially leading to biased estimates if not carefully selected. Moreover, ARDL ‎assumes that the relationships between variables are linear, which may not always accurately ‎capture the complex dynamics inherent in socioeconomic and environmental systems.‎

Similarly, the VECM technique has its own set of limitations. Firstly, it requires the time series ‎data to be stationary, which may not always hold true for real-world datasets. Additionally, ‎VECM assumes that all variables are cointegrated, implying a long-term relationship among ‎them. However, this assumption may not be put in certain cases, leading to inaccurate results. ‎Furthermore, VECM is sensitive to the presence of outliers and structural breaks in the data, ‎which can affect the reliability of the estimated coefficients.‎’’

Results and Discussion

15.  Enhance the clarity of the presentation of results, using tables, figures, and clear narrative descriptions to highlight key findings.

All modifications are colored in pink color.

16.  Deepen the analysis by linking the empirical results to the theoretical framework and research questions, discussing the implications and significance of the findings in detail.

The new modifications are colored in blue color as bellow:

‘’ The empirical results of the study provide valuable insights that can be linked to the theoretical framework and research questions, shedding light on the complex interplay between renewable energy, circular economy practices, environmental sustainability, and poverty reduction in China and the European Union (EU) . Firstly, the findings confirm the presence of long-run cointegration relations among the variables, validating the theoretical expectation of interconnectedness between environmental and socioeconomic factors over time. This supports the premise that sustainable development requires addressing both environmental degradation and poverty in tandem. Furthermore, the identification of mixed relationships among variables, including both unidirectional and bidirectional causal relationships, aligns with the theoretical understanding that the dynamics between renewable energy, circular economy practices, and poverty are multifaceted and context-dependent.

The empirical evidence also underscores the significance of regional variations in influencing the effectiveness of policy interventions. The differential impacts of factors such as solar power, circular economy measures, and fossil fuel consumption on CO2 emissions and poverty reduction between China and the EU highlight the importance of tailoring strategies to the specific socio-economic and environmental contexts of each region. This supports the theoretical notion that the success of sustainability initiatives is contingent upon understanding local conditions and implementing targeted measures accordingly.

Moreover, the implications of the findings extend beyond regional policy considerations to broader implications for global cooperation and knowledge-sharing. The recommendation for policymakers in both regions to prioritize data-driven approaches and establish knowledge-sharing platforms reflects the recognition of the interconnectedness of global challenges such as climate change and poverty. This aligns with the theoretical framework emphasizing the importance of collaborative governance and collective action at the international level to address shared sustainability challenges effectively.’’

17. Ensure that the discussion section thoroughly interprets the results in the context of the theoretical framework, existing literature, and the study's hypotheses or research questions.

We have tried to compare the founded results by another research. The new modifications (references) are colored in red color.

18.  Acknowledge the study's limitations and discuss alternative explanations for the findings to provide a balanced view.

We have tried to give the study's limitations and to discuss alternative explanations. All modifications are colored in blue-green color as bellow:

While this study sheds light on regional differences in factors affecting CO2 emissions and poverty reduction, some limitations need to be considered. Firstly, the study relates to existing research on poverty reduction impacts, which might not fully capture the complex social dynamics at play. Secondly, the reasons behind the varying effectiveness of renewable energy and circular economy measures between China and the EU are unclear. Further research is needed to explore these differences in policy implementation, infrastructure, and economic factors. An alternative explanation for the positive association between fossil fuels and poverty reduction in the EU could be due to specific industries or sectors relating on these fuels. Additionally, increased trade openness might be linked to knowledge or technology transfer that aids poverty reduction in developed economies. Overall, the study highlights the need for more nuanced analyzes and region-specific approaches that consider historical context, industrial structures, and ongoing sustainability efforts.

Conclusions and Policy Implications

19.  Connect the conclusions to the study's objectives, hypotheses, and key findings, ensuring a coherent narrative that ties back to the introduction.

The new modifications are as bellow (in green color):

Aligning with the research objective of understanding the effectiveness of renewable energy and circular economy practices in reducing poverty and environmental damage, this study employed a comparative approach between China and the EU. The findings confirmed the initial hypothesis that there's a complex interplay between these factors.

Firstly, the results supported the theoretical framework of sustainable development by demonstrating long-run cointegration between poverty, CO2 emissions, and economic factors. This highlights the interconnectedness of environmental and socio-economic issues, requiring a holistic approach to achieve sustainability. Secondly, the study identified both unidirectional and bidirectional causal relationships among the variables, reflecting the multifaceted nature of how renewable energy, circular economy, and poverty reduction interact - a key finding aligned with the research questions. Furthermore, the research emphasizes the importance of regional variations. China, for instance, saw a greater impact of solar power and circular economy measures on reducing CO2 emissions compared to the EU. This underscores the need for region-specific policies, as highlighted in the conclusion.

By highlighting these regional variations and the limitations of the current timeframe, the study pave the way for future research to delve deeper into the mechanisms at play and explore the long-term effects on poverty reduction. Overall, the research findings offer valuable insights for policymakers, supporting the call for data-driven, region-specific approaches, knowledge sharing platforms, and global cooperation to tackle poverty and climate change through renewable energy and circular economy practices.’’Haut du formulaire

20.  Ground policy recommendations firmly in the study's findings, providing specific, actionable, and feasible suggestions for policymakers and practitioners.

21.  Discuss the broader implications of the research, including its relevance to current challenges and potential contributions to the field.

The new modifications are colored in yellow color as bellow:

‘’ This research offers critical insights for policymakers in China and the EU, urging them to adopt regionally specific strategies to combat climate change and poverty. The study recommends that China significantly increase investments in solar power and circular economy initiatives, proven effective in reducing CO2 emissions and potentially alleviating poverty. For the EU, the focus should be on enhancing existing renewable energy and circular economy programs through better implementation, resource allocation, or technological advancements. Looking beyond regional approaches, the research emphasizes the importance of global collaboration to address these shared challenges. Establishing knowledge-sharing platforms would enable China and the EU to learn from each other's experiences with renewable energy, circular economy practices, and poverty reduction efforts. This collaboration, along with data-driven policymaking that leverages regional economic structures and environmental vulnerabilities, can lead to more effective global strategies. International cooperation is essential for developing and disseminating best practices to tackle global issues like climate change and poverty. This study significantly advances our understanding of the complex interplay between renewable energy, circular economy practices, and their impact on poverty and CO2 emissions. It highlights the crucial role of regional variations in designing sustainability policies and sheds light on the knowledge gap between China and the EU. By promoting knowledge sharing and collaboration, this research pave the way for faster progress in achieving sustainable development. The study also reinforces the theoretical framework of sustainable development by emphasizing the interconnectedness of environmental and socio-economic factors. Future research can explore the mechanisms by which these practices reduce poverty and investigate the long-term impacts beyond the timeframe explored here.’’Haut du formulaire

Review Report 4

Comments

Replies

1- what the finding of the paper is and  how this benefits the area of sustainability research.

For examples: 1) The literature review is a run-on paragraph that jumps from finding to finding without ever tying the papers together in a coherent means to establish expectations or hypotheses.

- We have added a new section ‘’ ‎6. Key Findings‎’’ to discus finding of the paper.

- We have added a new section ‘’Literature review’’.

2- Terms like "Poverty" and "Circular Economy", key to the paper, are never defined or given context or are their measurements defined and units given. For example, Line 329 states "with a decrease of 41.317 in poverty rate" but the units of poverty are never defined. Is this saying there is a 41% decline in the poverty rate? i.e. Poverty was 80% and now its 33%? or is it saying a decline of 41%? Or is it even showing a rate decline at all? I have no idea because units are not defined.

- Poverty was defined in Introduction section as bellow: ‘’ Poverty is one of the most significant issues, with over 9% of the global population living in ‎extreme poverty, earning less than $1.9 per day, according to the World Bank (2022). ‎’’

- we have added a new Table (Table 1: Variables summary) in order to explain all variables definition and measuring.

- we mean ''a decline of poverty rate'' and we have corrected the mistakes

Review Report 5

Comments

Replies

1- The literature review added strength to the study's background. The authors also improved the research design and methodology. In addition, the results and discussion sections are coherent., however, the authors should add more discussion about Table 7 in the discussion section.

We have tried to more explain the Table 8 (Table 7 becomes Table 8 after modifications requested by another reviewer) and the discussion was added in yellow color as bellow:

‘’ The dependant variable in the first model (CO2E) was colored in red color, however, The dependant variable in the second model (POV) was colored in yellow color. The double-headed arrows indicate that there is a bidirectional causal relationship between the dependent variable and the explicative variable. While the single-headed arrow indicates that there is a unidirectional relationship running from the explicative variable to the dependant variable.’’

2- However, the authors should write CO2 as CO2.

- Also, do not write 'we well...' as 'we'll.....'.  - In addition, in the text authors should write % as percent.

Only in tables and figures, it ought to be %.

- We have replaced CO2 by CO₂‎

- We have rewrite the paragraphs without using  'we well...' as 'we'll

- We have changed % in the text by ‘’percent’’

3- The paper should undergo proofreading as there are many typos throughout the manuscript, for example, in the text on page 12.

We have proofreading the paper and we have tried to correct the mistakes.