High-Quality Development and Decoupling Economic Growth from Air Pollution: Evidence from Daily Electricity Consumption in Fujian
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsAfter reviewing the manuscript titled “High-Quality Development and Decoupling Economic Growth from Air Pollution: Evidence from Daily Electricity Consumption in Fujian”, While the research question is significant and the methodology is innovative, especially in the measurement of HQDI, the manuscript in its current form has several limitations.
Key Argument or results:
The paper explores the impact of HQDI on the decoupling effect between economic growth and air pollution, the main findings are:
※HQDI can significantly promote decoupling, with innovation, openness, and sharing playing positive roles, while brown industries development and the electricity capacity installation of small and micro-enterprise hinder these processes. (no enough explanations have been provided)
※Extreme high temperatures exert a significant negative impact on decoupling, whereas increased market concentration fosters decoupling.(no enough explanations have been provided)
※The findings of this study provide important policy recommendations for balancing environmental governance with economic development. (no policy has been provided)
Validity:
The research question is highly essential, decoupling economic growth from air pollution is a key way for China to achieve “Carbon peak”。
Theory and Hypotheses
The author needs to further supplement the theoretical mechanism analysis of the impact of HQDI on the decoupling effect of economic growth and carbon emissions, and propose relevant research hypotheses.
Data and Methodology
The data sources are credible ( the Marketing Service Center of State Grid Fujian Electric 252 Power Co., Ltd, the National Bureau of Statistics, WAQHD, et al.). and meanwhile, Authors constructed the HQDI from the perspective of electricity consumption, which is different from the common method, However, the rationality of this measurement method and its advantages compared with existing methods need to be further explained.
Analytical Approach:
The author mainly used a panel data bidirectional fixed effects model to control for unit and time fixed effects, which is suitable for studying the impact of HQDI on decoupling effects. However, the author needs to add more control variables, replace explanatory variables (using the usual HQDI measurement instead of the HQDI measured in this article) to enhance the robustness of the results, add research on the mechanism of the impact of HQDI on decoupling from economic growth and carbon emissions (mediation effect model, moderation effect model), and add non-linear research on the impact of HQDI on the decoupling effect of economic growth and carbon emissions (such as threshold effect).
Conclusions
The conclusion needs to be more comprehensive.
Comments for author File: 
Comments.pdf
Author Response
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Comments 1: HQDI can significantly promote decoupling, with innovation, openness, and sharing playing positive roles, while brown industries development and the electricity capacity installation of small and micro-enterprise hinder these processes. (no enough explanations have been provided)
Response 1: Thank you for your thoughtful comments. We appreciate the opportunity to clarify and elaborate on our findings regarding the influence of sub-item development concept on the decoupling of economic growth and pollution emissions. In response, we revise Section 4.5 to provide more detailed explanations. Details as below. (Lines 680-724.)
This study also analyzes how sub-indicators in the three dimensions—innovation, green development, and coordinated development—impact decoupling. The results, detailed in Appendix Tables A2 and A3, demonstrate that all three dimensions of innovation, including digital economy development (digital industrialization and industrial digitization) and high-technology industry growth, significantly promote decoupling. By introducing clean technologies and more efficient production processes, innovation helps reduce pollution emissions while driving economic growth, fostering a decoupling effect. In contrast, the development of brown industries, as part of the green indicators, shows a significant negative impact. High-polluting, energy-intensive industries tend to have low production efficiency, and the cost of adopting cleaner energy or technologies is often prohibitive. These industries focus on immediate profits while neglecting environmental concerns. Consequently, an increase in the electricity consumption of brown industries signifies greater output but also leads to higher pollution emissions, hindering decoupling. On the other hand, green industries typically utilize clean energy or environmental technologies, meaning their growth contributes to lower pollution emissions, thus promoting decoupling. However, as the results show, the effect of green industries on decoupling is limited, likely due to their smaller share of total electricity consumption and relatively modest contribution to broader economic and environmental dynamics. Regarding coordinated development (regional and industrial coordination), we find that while reduced differences between regions and industries could improve resource allocation, energy use, industrial structure, and technological innovation, it does not always enhance decoupling. The absence of a clear shift toward cleaner industries or cleaner energy technologies means that coordination, without an emphasis on sustainability, may inadvertently lead to more pollution-intensive industrial activities. Therefore, if coordination leads to an industrial structure that favors high-polluting and high-emission sectors, it could limit the potential for decoupling. Openness, as indicated by the growth of the logistics industry, shows a positive influence on decoupling. An expansion of the logistics sector drives economic growth, but it also promotes smart logistics and efficient systems that reduce energy consumption and pollution. Moreover, logistical improvements lead to resource optimization, lower transportation costs, and reduced emissions, collectively contributing to the decoupling of economic growth from pollution. As for shared development, specifically the disparities in urban and rural electricity consumption and public infrastructure differences, we observe no significant impact on decoupling. The primary reason is that residential electricity consumption in urban and rural areas mainly supports daily living, with limited direct influence on economic growth and emissions. Similarly, infrastructure disparities reflect regional imbalances but do not directly affect the decoupling process. Additionally, the expansion of SMEs positively affects decoupling by driving market dynamism, promoting technological innovation and improving resource efficiency. In contrast, a reduction in SME capacity negatively impacts decoupling, likely because it diminishes economic activity and investment in environmental technologies. This, in turn, hinders decoupling efforts and further verifies the promotion of decoupling brought by expanding SMEs. All the results above support the hypothesis 2 in this study that all dimensions of high-quality development contribute to decoupling effect, while the development of brown industries may hinder the decoupling effect. We hope that these clarifications provide additional insight into the underlying mechanisms and the factors influencing decoupling. Thank you again for your valuable feedback.
Comments 2: Extreme high temperatures exert a significant negative impact on decoupling, whereas increased market concentration fosters decoupling. (no enough explanations have been provided)
Response 2: Thank you for your insightful comment. We appreciate your observation that the impact of extreme high temperatures and market concentration on decoupling requires further clarification. In response, we provide a more detailed explanation in the revised manuscript. The contents are presented in the third and fourth paragraph in Section 4.2. Details are as follows. (Lines 555-570.) The regression coefficients for the control variables reveal additional insights. Extreme high temperatures can negatively impact decoupling because they often lead to in-creased energy demand, particularly for cooling, which can strain energy systems and exacerbate environmental pollution. These weather extremes tend to heighten reliance on energy-intensive industries, especially in regions where cooling technologies are still largely dependent on non-renewable energy sources. This added strain makes it more challenging to decouple economic growth and pollution emissions, as it could increase energy consumption and pollution during peak temperature periods. Conversely, the increased market concentration (HHI) can foster decoupling because larger, more concentrated industries often have more resources to invest in energy-efficient technologies and adopt environmentally friendly practices. In such industries, the potential for economies of scale may lead to more effective implementation of green technologies and stricter adherence to environmental regulations, thus reducing pollution intensity. The higher the market concentration, the more these firms can leverage innovations and technological advancements that contribute to decoupling economic growth from pollution. We elaborate on these points in the revised manuscript to clarify the mechanisms through which extreme temperatures and market concentration affect decoupling. We believe this additional explanation enhances the clarity and robustness of our findings. Thank you again for your valuable feedback.
Comments 3: The findings of this study provide important policy recommendations for balancing environmental governance with economic development. (no policy has been provided)
Response 3: Thank you for your valuable comment. We have added a second paragraph to the conclusion section, where we discuss the policy implications of our study’s findings. The revised content is as follows. (Lines 739-753.) Based on the study’s findings, several policy recommendations are proposed to promote sustainable economic growth while decoupling it from pollution emissions. Firstly, innovation and openness should be incentivized through R&D support and industry–university collaborations, fostering the adoption of cleaner technologies. Additionally, stronger policies to support green industries and environmentally friendly technologies are essential, as brown industries hinder decoupling. Energy efficiency standards for SMEs should be implemented, alongside financial and technical assistance, to reduce their energy consumption. To address the negative impacts of extreme weather, climate-resilient infrastructure and increased use of renewable energy should be prioritized. Furthermore, promoting market concentration can enhance energy efficiency and sustainability, though regulatory oversight is necessary to maintain competition. Expanding renewable energy usage and refining the HQDI to include emerging factors like digital technologies and circular economies will also support decoupling. These integrated policy measures aim to guide Fujian Province and similar regions toward a balanced and sustainable development trajectory. Thanks again for your insightful feedback. We believe that these policy recommendations will offer practical guidance for balancing environmental governance with economic development, making our findings more applicable to policymakers.
Comments 4: Validity. The research question is highly essential, decoupling economic growth from air pollution is a key way for China to achieve “Carbon peak”.
Response 4: Thank you for your positive comment. We appreciate your recognition of the importance of the research question. Indeed, decoupling economic growth from air pollution is a critical strategy for China to achieve its "carbon peak" goals and transition towards a more sustainable development path. We believe that our study contributes to this important discourse by providing empirical evidence and theoretical insights on how high-quality development can support this decoupling process. We hope that our findings will inform both policy design and future research aimed at promoting sustainable economic growth while mitigating environmental impacts. Thank you again for your valuable feedback.
Comments 5: Theory and Hypotheses. The author needs to further supplement the theoretical mechanism analysis of the impact of HQDI on the decoupling effect of economic growth and carbon emissions, and propose relevant research hypotheses.
Response 5: Thank you for your constructive comments on the Theory and Hypotheses section. In response to your suggestion, we expand the theoretical mechanism analysis regarding the impact of HQDI on the decoupling effect of economic growth from pollution emissions. Specifically, we incorporate relevant literature from Wang et al. (2020), Khan et al. (2022), Ren et al. (2022), Xiao et al. (2022), Xu et al. (2022), and Zhang and Ran (2023) to illustrate how different dimensions of HQDI may influence the decoupling effect. Building on this, we propose two research hypotheses. Furthermore, to enhance the clarity and structure of the paper, we adjust it to the previous sections. Section 2.1 now introduces the measurement of the decoupling from air pollution, and Section 2.2 is revised to focus on the measurement of HQDI, followed by a discussion of existing literature on how various aspects of HQDI influence the decoupling effect. All contents are as follows. (Lines 159-304.) 2.1 Measurement of the decoupling from air pollution The relationship between economic growth and pollution emissions frequently manifests as a complex dynamic. Grossman and Krueger (1995) [7] initially proposed an inverted U-shaped relationship between economic growth and pollution emissions known as the Environmental Kuznets Curve (EKC). This theory has garnered support from numerous studies indicating that during the nascent stages of economic development, there is an increase in pollution emissions concomitant with economic growth. However, as the economy attains a certain level of development, pollution emissions gradually decline in conjunction with ongoing economic growth [8, 9]. Furthermore, with technological progress, policy interventions, and industrial restructuring, economic growth and pollution emissions may achieve decoupling, i.e., the separation between economic growth and negative environmental impacts (e.g., resource consumption, pollution emissions). However, Lazăr et al. (2019) [10] identify a complex, nonlinear relationship between GDP and CO2 emissions in Central and Eastern European countries, which can take various forms, including N-shaped, inverted-N, U-shaped, inverted-U, monotonic, or even no statistical link. Mughal et al. (2022) [11] further confirm there is an inverted-U-shaped Environmental Kuznets Curve (EKC) relationship between economic growth and CO2 emissions in selected South Asian economies. When studying the correlation between transportation CO2 emissions and GDP in 15 EU countries, Tapio (2005) [12] employs the elasticity coefficients of carbon emissions and economic growth to construct the Tapio decoupling model, which includes three types of states: decoupling, coupling, and negative decoupling. According to the elastic relationship between GDP and carbon emissions, the decoupling state is further subdivided into eight types. When the elastic relationship between GDP and carbon emissions is coupled, if both grow positively, it is considered expansionary coupling; otherwise, it is implicit coupling. Conversely, when GDP and carbon emissions are decoupled, if GDP grows and carbon emissions decrease, it is strong decoupling. If both increase, it is weak decoupling; otherwise, it is implicit decoupling. If the elastic relationship indicates negative decoupling, in which GDP is in recession and carbon emissions increase, it is considered strong negative decoupling. If both increase, it is expansionary negative decoupling; otherwise, it is weak negative decoupling. The Tapio decoupling model addresses the effect of base period selection on decoupling results and refines the delineation of decoupled states by introducing the elasticity coefficient [13]. Consequently, scholars have increasingly adopted the model to analyze the relationship between economic growth and energy consumption or pollution emissions. For instance, Naqvi and Zwickl (2017) [14] utilize the Tapio model to investigate the decoupling status between economic growth and pollution emissions in EU countries across. Similarly, Shuai et al. (2019) [15] employ the Tapio model to investigate 133 countries, focusing on the decoupling status of economic growth and total carbon emissions, carbon emission intensity, and positive per capita carbon emissions. Luo et al. (2021) [16] utilize the Tapio model to analyze the decoupling status of economic growth and resources and the environment in the Central Plains Urban Agglomeration in China. Furthermore, several studies have investigated the factors influencing the decoupling effect using models such as the Logarithmic Mean Divisia Index (LMDI) and Structural Decomposition Analysis (SDA). For instance, Engo (2018) [17] utilizes the Tapio and LMDI methods, based on an extended Kaya identity, to assess the decoupling of economic growth and CO2 emissions in Cameroon from 1990 to 2015. Song et al. (2019) [18] develop a two-dimensional decoupling theory between economic development and COâ‚‚ emissions and investigate the turning points of the impact of per capita GDP on carbon emissions in China and the United States. Yang et al. (2021) [19] combine the Tapio method and LMDI to investigate the differences in the decoupling index and explore the key drivers of decoupling and their contributions to the decoupling index across six continents and major regions during the period 2001-2017. Dong et al. (2021) [20] employ the Tapio model and the C-D-Kaya equation to investigate the factors influencing the emergence of new and original decoupling indicators. Khan and Majeed (2023) [21] analyze Pakistan’s decoupling from 1980 to 2018, finding Expensive Negative Decoupling (END) driven by carbon intensity and urbanization. Yuan et al. (2024) [22] adopt the Tapio model to examine the nexus between economic growth and resource-environmental pressure in 110 cities situated within the Yangtze River Economic Belt (YREB). Riveros and Shahbaz (2024) [23] apply the TAPIO model, Kaya identity, and LMDI to study Colombia’s decoupling from 1975 to 2021, focusing on economic structure and energy consumption. Jia et al. (2024) [24] also combine LMDI and the Tapio model to evaluate the carbon emissions and sinks in the Yellow River basin. In recent years, China has demonstrated a pattern of either weak or strong de-coupling of economic growth and pollution emissions across a range of industrial sectors [13, 25]. The Tapio model effectively characterizes the relationship between economic growth and pollution emissions, particularly by categorizing different decoupling states. However, it is limited to state analysis and does not capture the causal relationship between economic growth and emissions. To overcome this, it should be combined with other models for a more comprehensive analysis. Additionally, the model is highly reliant on data accuracy and completeness. Many countries and regions face challenges related to incomplete or inaccurate data collection, which can impact the model’s reliability. Furthermore, the applicability of the Tapio model may vary depending on regional differences in data quality and availability, which can restrict its use in diverse contexts. 2.2 High-quality development and the decoupling of economic growth from air pollution In recent years, China has implemented a high-quality development strategy aimed at achieving sustainable economic growth by enhancing economic quality, optimizing industrial structures, and strengthening innovation capabilities. This strategy is underpinned by five core principles: innovation, green development, coordination, openness, and sharing, which serve as key dimensions for evaluating development quality. Existing studies employ diverse methodologies and indicators to assess high-quality development. Some use single indicators such as total factor productivity (TFP) or green total factor productivity (GTFP) [2, 26], while others adopt multidimensional frameworks. For instance, Peng et al. (2021) [27] use factor analysis at the prefecture level, focusing on economic scale, structure, efficiency, and welfare. Pan et al. (2021) [28] construct a HQDI incorporating five domains: economic growth, innovation efficiency, environmental impact, ecological services, and livelihoods. Luo et al. (2023) [29] assess enterprise-level development using six dimensions: innovation, greenness, openness, sharing, efficiency, and risk prevention. Chen et al. (2024) [4] extend these principles by adding indicators related to growth momentum and deepened reforms, while Han and Cao (2024) [30] construct a framework to the marine economy, focusing on innovation, coordination, green development, openness, and sharing. These studies contribute to developing robust evaluation systems for high-quality development, and the entropy approach has been employed to construct HQDI, providing robust tools for assessing regional economic development. High-quality development impacts economic growth and pollution emissions in multiple ways. Innovation is a key driver, enhancing productivity by increasing total factor productivity (TFP) [31, 32]. It boosts economic growth through improved competitiveness and structural transformations. Wang et al. (2020) [33] find that technological progress in China’s high-tech industries helps reduce embodied carbon emissions, particularly in the electrical and optical equipment sector. Chen et al. (2022) [5] argue that technological innovation promotes green technology adoption, driving economic growth and improving environmental performance. The digital economy, incorporating technologies like big data, AI, and IoT, plays an important role in both economic growth and environmental sustainability. It improves productivity by optimizing production processes and creating new economic activities. Ma and Zhu (2022) [34] demonstrate that the digital economy can enhance green development by improving the efficiency of resource use and facilitating the spread of green technologies across industries. Xu et al. (2022) [35] and Zhang and Ran (2023) [36] also reveal that the digital economy not only improves industrial structures but also contributes to pollution reduction. Moreover, the digital economy also emerges as a crucial driver of technological innovation, particularly in improving productivity [28], and contributing to reductions in carbon emissions and pollution [37]. Green development is another crucial aspect of high-quality development. It emphasizes environmental protection through the adoption of cleaner technologies and sustainable practices. Research consistently shows that green development contributes to reducing pollution emissions while promoting economic growth. For instance, Peng et al. (2021) [27] show that green innovation, especially in industries such as renewable energy, produces positive spillover effects both spatially and across sectors, thereby improving overall environmental quality. Ren et al. (2022) also highlight that green investment boosts energy conservation and emission reduction, upgrading industrial structures and fostering technological innovation. Ma et al. (2022) [6] show that green technology adoption reduces carbon emissions while fostering economic growth, aligning economic performance with environmental sustainability. On the contrary, the development of brown industries will increase pollution emissions and promote economic growth through economies of scale. At the same time, it will lead to an increase in pollution emissions because these industries may ignore environmental protection. Coordinated regional development plays a vital role in sustainable development. Studies such as those by Xiao et al. (2022) [38] reveal that coordinated regional development, such as Beijing-Tianjin-Hebei area, significantly impacts pollution reduction. The coordinated efforts lead to more efficient resource distribution, better industrial structures, and the adoption of cleaner technologies, which help reduce emissions. Furthermore, trade openness and foreign direct investment (FDI) are found to have negative associations with carbon emissions, as they often facilitate the transfer of cleaner technologies and enhance institutional quality, further promoting environmental sustainability [39]. Sharing, by optimizing industrial structures and ensuring resource allocation, can also promote economic growth and pollution reduction. While existing research offers valuable insights, several gaps remain. Few studies comprehensively examine how technological innovation, digital economy, green development, and coordination interact synergistically to promote sustainable development. This highlights the need for further exploration of the mechanisms that underpin the relationship between high-quality development and decoupling. Based on this literature, we propose the following hypotheses to explore the role of high-quality development in decoupling economic growth from pollution emissions: Hypothesis 1: High-quality development contributes to achieving the decoupling effect. Hypothesis 2: All dimensions of high-quality development contribute to decoupling effect, while the development of brown industries may hinder the decoupling effect.
Additionally, to address the inclusion of the new hypotheses, we have incorporated relevant explanations in the empirical results section. Specifically:
We believe that these modifications improve the overall structure and theoretical rigor of the paper. Thank you again for your valuable suggestions.
Comments 6: Data and Methodology. The data sources are credible ( the Marketing Service Center of State Grid Fujian Electric 252 Power Co., Ltd, the National Bureau of Statistics, WAQHD, et al.). and meanwhile, Authors constructed the HQDI from the perspective of electricity consumption, which is different from the common method, However, the rationality of this measurement method and its advantages compared with existing methods need to be further explained.
Response 6: Thank you for your valuable comment. We appreciate your recognition of the credibility of our data sources, including the Marketing Service Center of State Grid Fujian Electric Power Co., Ltd, the National Bureau of Statistics, WAQHD, and others. Regarding your concern about the construction of the HQDI from the perspective of electricity consumption, we understand that this approach may differ from more traditional methods. To address this, we provide a more detailed explanation of the rationale behind our measurement method and its advantages over existing approaches. This discussion is supplemented in the introduction of the revised manuscript. The contents are as follows. (Lines 112-145.) This study makes several significant contributions, as outlined below. First, to address the dearth of monthly industry data, this study employs an innovative methodology that combines input–output tables and big data on electricity to estimate regional output and the scale of the digital economy. On the one hand, the input–output table elucidates the technical and economic interconnections between sectors. Conversely, as a “barometer” of economic activity, the big data on electric power directly correlates with both economic development and environmental sustainability. These data can reflect economic dynamics with greater precision and in real time, thereby compensating for the limitations of traditional data and enhancing insights into the dynamics of economic structure changes. Secondly, by incorporating electricity consumption data, we can capture not only traditional economic growth indicators but also measure the activity of small and micro-enterprises, the structure of industries, the development of high-tech sectors, and the scale of digital industrialization. These factors are not typically available through conventional macroeconomic indicators and offer a more comprehensive view of high-quality development and innovation. Thus, these factors enhance our ability to understand the decoupling relationship between economic growth and pollution, which may be overlooked when relying solely on traditional measures such as GDP. Thirdly, given that most research focuses on traditional macroeconomic factors, such as energy consumption intensity, industrial structure, and population size, this study explores the dynamic relationship between economic growth and the decoupling of pollution emissions by refining industry-level data. The monthly year-on-year growth of pollution emissions and GDP is calculated using daily electricity consumption data and the Tapio model, eliminating seasonality and economic cycle effects and accurately capturing the influence of changes in industrial structure on the decoupling effect. Fourthly, given the representative nature of Fujian Province with respect to economic and environmental governance, this study investigates the impact of HQDI on the decoupling effect at the prefecture level. This analysis reveals how high-quality development promotes the coordination of economy and environment, providing theoretical support and empirical evidence for other regions to achieve sustainable economic development. Finally, the methodology proposed in this study applies to regions outside China with similar characteristics if relevant data such as regional electricity consumption and price are available. The core input–output analysis method, supported by global databases like EORA, WIOD, and EXIOBASE, can be utilized to extend this approach to other regions, contributing to the broader literature. We hope that this expanded explanation clarifies the rationale and advantages of our approach. Thank you again for your insightful feedback, which helps us strengthen the foundation of our methodology.
Comments 7: Analytical Approach. The author mainly used a panel data bidirectional fixed effects model to control for unit and time fixed effects, which is suitable for studying the impact of HQDI on decoupling effects. However, the author needs to add more control variables, replace explanatory variables (using the usual HQDI measurement instead of the HQDI measured in this article) to enhance the robustness of the results, add research on the mechanism of the impact of HQDI on decoupling from economic growth and carbon emissions (mediation effect model, moderation effect model), and add non-linear research on the impact of HQDI on the decoupling effect of economic growth and carbon emissions (such as threshold effect).
Response 7: Thank you for your constructive comments. We appreciate your thoughtful suggestions to enhance the robustness of our analysis. In response to your comments, we make several revisions and clarifications. First, due to difficulties in obtaining certain more control variables to measure monthly economic indicators for prefecture-level cities, such as energy consumption structure, industrial structure, and policy variables, we add city fixed effect and year fixed effects to alleviate the omitted variable bias. Details are presented in the first paragraph in Section 4.3. Details as below. (Lines 571-604.) As we found, HQDI can increase the likelihood that cities will achieve a decoupling state. However, it is necessary to discuss the validity of the adopted econometric model. The econometric model presented in this paper (Equation 8) may be subject to two potential issues. First, due to difficulties obtaining specific monthly economic indicators for prefecture-level cities, such as energy consumption structure, industrial structure, and policy variables, both the HQDI and decoupling effects could be influenced simultaneously. As a result, endogeneity problems may arise from the omitted variables. To address this, we control for both year and city fixed effects, which helps mitigate the impact of omitted variable bias on our estimation results. Second, while HQDI influences the decoupling effect, the decoupling effect itself captures an optimal state where economic growth occurs alongside reduced pollution emissions. This relationship may, in turn, affect HQDI, potentially leading to reverse causality and endogeneity concerns. To mitigate these concerns, we adopt instrumental variables, specifically the lagged term of the HQDI and the average HQDI of cities other than the focal city, as commonly practiced in literature. The rationale for utilizing two instrumental variables is that when there is only one endogenous variable, the exogeneity of the instrumental variables can be tested using Hansen J statistic. The results of the instrumental variable regression are presented in Table 5. The results in Table 5 suggest that the two instrumental variables significantly affect HQDI in the first stage at the 1% confidence level. Moreover, the Kleibergen–Paap rk Wald F statistic is 102.229, well above 10, indicating no issue of a weak instrumental variable. In the second stage, the P-value of the Hansen J statistic is 0.388, indicating that we cannot reject the null hypothesis of exogeneity for all instrumental variables, confirming their relevance and exogeneity. Regarding the regression coefficients, the impact of HQDI on decoupling from PM2.5 remains significantly positive at the 1% level, confirming the robustness of our results. The HQDI coefficient of 0.479 is 1.86 times higher than the benchmark regression value of 0.258, suggesting that potential endogeneity may lead to an underestimation of HQDI’s effect on decoupling. This underestimation is within an acceptable range, further supporting the robustness of our findings. For other robustness test, we add sensitivity analysis by adjusting the threshold of extreme high temperatures from 28℃ to 35℃. Results are presented in Section 4.4.2 Details as below. (Lines 641-661.) In the benchmark regression, we define extreme high-temperature days as the total number of days per month with a maximum temperature of 30°C or higher. This definition assumes that when temperatures exceed 30℃, industrial equipment consumes additional energy for cooling, leading to higher overall energy consumption, which could influence the decoupling effect. The results indeed show that an increase in extreme high-temperature days reduces the decoupling effect between economic growth and pollution emissions. However, due to the difficulty in defining a precise threshold for extreme high temperatures, we conduct a robustness check by redefining extreme high-temperature days as those between 28°C and 35°C. The results are presented in Table 7. The results of this robustness test, shown in Table 7, reveal that even with this modified definition, HQDI continues to significantly increase the decoupling effect. Additionally, the negative impacts of extreme high-temperature days and the Herfindahl–Hirschman Index on decoupling remain statistically significant, consistent with the benchmark results. This sensitivity analysis indicates that the conclusions drawn in this study are robust and not sensitive to the specific choice of extreme high-temperature threshold. Regardless of how extreme high-temperature days are defined, HQDI has a robust and positive effect on decoupling, while extreme temperatures remain a significant factor hindering the decoupling of economic growth and pollution emissions. For the mechanism analysis, we investigate all the sub-item of HQDI on the decoupling effect, which explain how these items affect the decoupling effect, revealing how the high-development concept promote decoupling. This analysis helps to clarify the technological innovation, green development and openness mediate the relationship between HQDI and decoupling. We add non-linear research on the impact of HQDI on the decoupling effect of economic growth and pollution emissions. Results are presented in column (6) of Table 6. The updated robustness of in Table 6 and the explanations are presented as below. (Lines 606-639.)
4.4.1 Robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification To verify the robustness of the benchmark results, this study conducts several robustness tests, including replacing the explained variable, involving outlier removal, adjusting the econometric model, and modifying the control variables. First, we redefine the decoupling from SO2 and decoupling from PM10 to reveal whether the impact of HQDI on the decoupling effect from different pollutants is still held. The results are shown in columns (1) and (2) of Table 6. Second, to ensure the prediction method does not bias the results, Zhangzhou is excluded from the analysis due to significant gaps in its pollution emission data, which are estimated using data from other cities and local temperature information in the baseline analysis; the results are reported in column (3) of Table 6. Third, given that the explanatory variables are binary indicators of strong or weak decoupling between economic growth and pollution emissions, a probit model is adopted to assess whether the findings are sensitive to the choice of regression model. The results of this analysis are presented in column (4) of Table 6. Then, the measurement of industry concentration is replaced with the market share percentage of the top four industries to provide a more precise representation of regional industry concentration, as shown in column (5) of Table 6. Finally, we add nonlinear research into HQDI on the decoupling effect, as shown in column (6) of Table 6. The results in the first two columns in Table 6 suggest that HQDI positively impacts the decoupling from SO2 and PM10 at the 1% confidence level, confirming the robustness of HQDI on the decoupling effect from different pollutants. Moreover, the magnitude of coefficients reveals that a 1% increase in HQDI can increase the probabilities of decoupling from SO2 and PM10 by 44% and 42%, respectively. These findings underscore the role of HQDI in promoting sustainable development driven by innovation, green development, and technological progress. Fujian Province has actively embraced high-quality development principles, adjusting its industrial and energy structures to support decoupling efforts. Moreover, the results in columns (3)–(5) in Table 6 indicate that HQDI has a significant positive impact on the decoupling effect of PM2.5 at the 1% confidence level, thereby validating the reliability of the benchmark findings.
Table 6. Results of robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification.
We hope these revisions address your concerns and further strengthen the analysis. Thank you once again for your insightful suggestions, which have contributed to the refinement of our research.
Comments 8: Conclusions. The conclusion needs to be more comprehensive.
Response 8: Thank you for your helpful comment. In response to your suggestion to provide policy implications for our findings as well as the discussions raised by other reviewers regarding the study’s limitations and the applicability of the methodology, we expand and enrich the conclusion section. The revised content is as follows. (Lines 726-768.) This study conducts a comprehensive investigation into the influence of high-quality development on the decoupling effect between economic growth and pollution emissions across nine prefecture-level cities in Fujian Province. By employing the input–output analysis method, this study calculates the direct electricity consumption coefficients of various industries and determines the total regional output using industrial electricity consumption and electricity price data. This study constructs a decoupling index using the Tapio decoupling method, while the entropy method is utilized to build an HQDI grounded in the five principles of innovation, green, coordination, openness, and sharing. The findings reveal that HQDI significantly contributes to achieving decoupling, with innovation, openness, and sharing playing crucial roles. On the other hand, brown industry development and the installation of electricity capacity by SMEs negatively impact the decoupling process. Extreme temperatures also exert a substantial adverse effect, while increased market concentration supports decoupling. Based on the study’s findings, several policy recommendations are proposed to promote sustainable economic growth while decoupling it from pollution emissions. Firstly, innovation and openness should be incentivized through R&D support and industry–university collaborations, fostering the adoption of cleaner technologies. Additionally, stronger policies to support green industries and environmentally friendly technologies are essential, as brown industries hinder decoupling. Energy efficiency standards for SMEs should be implemented, alongside financial and technical assistance, to reduce their energy consumption. To address the negative impacts of extreme weather, climate-resilient infrastructure and increased use of renewable energy should be prioritized. Furthermore, promoting market concentration can enhance energy efficiency and sustainability, though regulatory oversight is necessary to maintain competition. Expanding renewable energy usage and refining the HQDI to include emerging factors like digital technologies and circular economies will also support decoupling. These integrated policy measures aim to guide Fujian Province and similar regions toward a balanced and sustainable development trajectory. The decision to focus on Fujian Province in this study stems primarily from the availability of extensive electricity consumption data, which provides a solid foundation for our analysis. Additionally, Fujian’s industrial structure and significance within the broader national context make it a representative case for understanding high-quality growth strategies and decoupling processes. However, this focus on a single province is also a limitation of the study, as the findings may not be fully generalizable to regions with different industrial profiles or energy dynamics. Fortunately, the proposed methodology will be applied to other regions or countries once electricity consumption and price data are available. Future research could expand the geographical scope and incorporate additional variables, such as renewable energy usage, innovation capabilities, and policy interventions, to offer a more holistic understanding of decoupling dynamics across diverse regions. Moreover, investigating the impact of green technologies and government policies on decoupling could provide valuable insights into how sustainable economic growth can be further promoted, shedding light on the complex relationship between economic development and environmental sustainability. We believe that these expanded policy recommendations and the discussions on limitations significantly enhance the comprehensiveness of the study. Thanks again for your insightful comments and suggestions, which have helped us refine and strengthen the conclusion. |
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Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsThere are some comments to improve your manuscript.
(1) Some comparison with other works should be given in "Results and discussion". One of the main aims is to show your difference with other existing works.
(2) Some experiments or analysis should be done to test the accuracy of your models. Certainly, some data and results must be given as the proofs.
(3) Where your data comes from?
(4) Some parameters should be not written in italic type. By contrast, some parameters should be written in italic type. Therefore, all parameters should be checked carefully. All parameters in equations should keep unanimous with them in sentences (including Abbreviations).
(5) Can your methods or models be extended across our country? Why?
Author Response
Comments 1: Some comparison with other works should be given in "Results and discussion". One of the main aims is to show your difference with other existing works.
Response 1: Thank you for your insightful comment. We agree that comparing our findings with existing works is important for contextualizing our results and highlighting the contributions of our study. In response, we add a detailed comparison with relevant studies in Section 5. (Lines 726-768.)
This study conducts a comprehensive investigation into the influence of high-quality development on the decoupling effect between economic growth and pollution emissions across nine prefecture-level cities in Fujian Province. By employing the input–output analysis method, this study calculates the direct electricity consumption coefficients of various industries and determines the total regional output using industrial electricity consumption and electricity price data. This study constructs a decoupling index using the Tapio decoupling method, while the entropy method is utilized to build an HQDI grounded in the five principles of innovation, green, coordination, openness, and sharing. The findings reveal that HQDI significantly contributes to achieving decoupling, with innovation, openness, and sharing playing crucial roles. On the other hand, brown industry development and the installation of electricity capacity by SMEs negatively impact the decoupling process. Extreme temperatures also exert a substantial adverse effect, while increased market concentration supports decoupling.
Based on the study’s findings, several policy recommendations are proposed to promote sustainable economic growth while decoupling it from pollution emissions. Firstly, innovation and openness should be incentivized through R&D support and industry–university collaborations, fostering the adoption of cleaner technologies. Additionally, stronger policies to support green industries and environmentally friendly technologies are essential, as brown industries hinder decoupling. Energy efficiency standards for SMEs should be implemented, alongside financial and technical assistance, to reduce their energy consumption. To address the negative impacts of extreme weather, climate-resilient infrastructure and increased use of renewable energy should be prioritized. Furthermore, promoting market concentration can enhance energy efficiency and sustainability, though regulatory oversight is necessary to maintain competition. Expanding renewable energy usage and refining the HQDI to include emerging factors like digital technologies and circular economies will also support decoupling. These integrated policy measures aim to guide Fujian Province and similar regions toward a balanced and sustainable development trajectory.
The decision to focus on Fujian Province in this study stems primarily from the availability of extensive electricity consumption data, which provides a solid foundation for our analysis. Additionally, Fujian’s industrial structure and significance within the broader national context make it a representative case for understanding high-quality growth strategies and decoupling processes. However, this focus on a single province is also a limitation of the study, as the findings may not be fully generalizable to regions with different industrial profiles or energy dynamics. Fortunately, the proposed methodology will be applied to other regions or countries once electricity consumption and price data are available. Future research could expand the geographical scope and incorporate additional variables, such as renewable energy usage, innovation capabilities, and policy interventions, to offer a more holistic understanding of decoupling dynamics across diverse regions. Moreover, investigating the impact of green technologies and government policies on decoupling could provide valuable in-sights into how sustainable economic growth can be further promoted, shedding light on the complex relationship between economic development and environmental sustainability.
Thanks again for your valuable suggestion, which has significantly improved the clarity and depth of our discussion.
Comments 2: Some experiments or analysis should be done to test the accuracy of your models. Certainly, some data and results must be given as the proofs.
Response 2: Thank you for your valuable comment. We completely agree that testing the accuracy of our models is crucial for ensuring the robustness of the results. In response to your suggestion, we compare the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province to evaluate the accuracy of our models. Both datasets are adjusted to constant prices, and the results are presented in Figure 2. As shown in the last paragraph of Section 3.2.1. The contents are shown below. (Lines 382-395.)
In our sample period, global financial crises and the COVID-19 pandemic may have introduced potential biases in the data. To address this, we compare the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province to evaluate the accuracy of our models. Both datasets are adjusted to constant prices, and the results are presented in Figure 2. As Figure 2 shows, the GDP shows a certain degree of seasonality, with GDP increasing slightly from the first to the third quarter, while the fourth quarter sees the highest level of fluctuations. The estimated GDP values follow a similar pattern, with both trends closely aligned. Moreover, the estimated GDP is slightly lower than the actual GDP, with the difference being less than 2%. This consistency further supports the validity of using electricity consumption and direct consumption coefficients to forecast industry value-added trends in Fujian Province.
Figure 2. Comparison of GDP provided by the National Bureau of Statistics of China and estimated GDP.
We hope these additional tests provide the necessary evidence to support the accuracy and robustness of our models. Thank you again for your insightful suggestion, which allows us to further clarify the methods and justify our approach to handling data and forecasting.
Comments 3: Where your data comes from?
Response 3: Thank you for your question regarding the data sources. The dataset used in this study consists of multiple data sources. We add the sources of temperature in the revised manuscript and the data sources are presented in Section 3.1. Details of data sources are shown below. (Lines 306-348.)
Electricity Consumption and Electricity Price Data: Daily electricity consumption and electricity price data are sourced from the Marketing Service Center of State Grid Fujian Electric Power Co., Ltd. These cover 133 sub-sector industries and 72 medium-category industries.
Direct Electricity Consumption Coefficients: These coefficients are derived from the Fujian Province input-output analysis, which is based on the China Regional Input-Output Tables compiled by the Department of National Economic Accounting of the National Bureau of Statistics.
Air Pollution Emission Data: The primary source of air pollution emission data is the World Air Quality Historical Database (WAQHD), which provides air quality data from over 250,000 monitoring stations in more than 2,000 cities worldwide. This database includes pollutants like PM2.5, PM10, SO2, NOx, CO, and O3.
Temperature Data: Daily temperature data, adopted for defining extreme high-temperature days, are sourced from the Tianqihoubao website (http://tianqihoubao.com/).
Market Structure Data: The Herfindahl-Hirschman Index (HHI), which measures industry concentration, is calculated based on industry value-added data.
These data sources collectively support the analysis of the decoupling effect and related factors in Fujian Province. We hope this clarifies the origins of the data used in our study. The content of Section 3.1 is shown below. (Lines
3.1 Data and variable definitions
This study adopts a monthly frequency for analysis, with all monthly indicators derived from daily data. This approach mitigates the noise inherent in daily data, which can compromise the accuracy of estimation results and the effectiveness of identification. By utilizing monthly data, this analysis better captures overall trends, minimizes the impact of short-term fluctuations, and enhances the robustness and explanatory power of the model estimates. The dataset consists primarily of monthly regional production and pollution emission data from January 2019 to June 2024. Daily power consumption and electricity price data, used to calculate the output value of prefecture-level cities in Fujian Province, are sourced from the Marketing Service Center of State Grid Fujian Electric Power Co., Ltd., covering 133 sub-sector industries and 72 medium-category industries. The direct electricity consumption coefficients necessary for regional output calculations are derived from the Fujian Province input–output analysis based on the China Regional Input–Output Tables compiled by the Department of National Economic Accounting of the National Bureau of Statistics. These tables are developed using uniform techniques, with provincial statistical offices generating regional tables accordingly. Details on the calculation of prefecture-level city output values are provided in subsection 3.2.1.
The World Air Quality Historical Database (WAQHD) serves as the primary source of air pollution emission data for prefecture-level cities. This comprehensive database provides air quality information for over 130 countries, encompassing more than 250,000 monitoring stations in 2,000 cities. The WAQHD dataset encompasses a wide range of pollutants, including PM2.5, PM10, SO2, NOx, CO, and O3. For regions with minimal missing data, linear interpolation is used to address gaps, while in Zhangzhou, where data gaps are more significant, multiple linear regression is employed to estimate missing pollution emissions by regressing available data on emissions in other regions and local meteorological variables (e.g., maximum and minimum temperatures). The model-generated values are then used to fill out missing data.
The conversion of pollution emission values, originally expressed in units such as milligrams per cubic meter or parts per million, into pollutant-specific AQIs is conducted in accordance with the standards established by the United States Environmental Protection Agency (US EPA). Monthly emissions are calculated by averaging the daily AQI values, thereby reflecting average monthly air quality. The primary focus of this study is the examination of the decoupling effect of economic growth and PM2.5 emissions, focusing on robustness tests centered on SO2 and PM10 (refer to subsection 3.2.2). The core explanatory variable, the regional high-quality development index (HQDI), is defined in subsection 3.2.3. Control variables include monthly extreme high temperatures (number of days with maximum temperatures exceeding 30°C), as such conditions increase electricity demand, particularly for cooling, leading to higher energy consumption and emissions without corresponding output increases. The original daily temperature data are sourced from the Tianqihoubao website (http://tianqihoubao.com/). The impact of market structure on the decoupling relationship is also controlled by industry concentration, measured by the Herfindahl–Hirschman Index (HHI), which is calculated based on the industry value added. Variable definitions are detailed in Table 1.
Thanks again for your insightful questions.
Comments 4: Some parameters should be not written in italic type. By contrast, some parameters should be written in italic type. Therefore, all parameters should be checked carefully. All parameters in equations should keep unanimous with them in sentences (including Abbreviations).
Response 4: Thank you for your helpful comment. We have carefully reviewed all parameters in the study to ensure consistency in formatting. Specifically, we present all parameters in equations uniformly with their corresponding terms in the text and apply the correct use of italicization. We also verify that abbreviations are consistently formatted throughout the manuscript, following standard conventions. These adjustments will be made to enhance the clarity and consistency of the paper.
First, DEC is the abbreviation of the decoupling elasticity coefficient. Hence, it should not be present in italicization. We correct all DEC to be non-italic type in Equation (4) (line in 406). Also, DEC in the first row and fifth column of Table 2 is corrected to be non-italic type.
Second, HQDI is the abbreviation of high-quality development index, which should not be written in italic type. We correct it in third paragraph of Section 3.4. The content is as follows. HQDI in equation (7) and (8), as well as the explanation of Equation (8), are corrected in non-italic type, as seen in lines 485, 489, and 491. Additionally, HQDI in the titles of Tables 4, 8, and A2–A3 has been corrected to non-italic type, as seen in lines 552, 679, 787 and 790.
Third, terms “n-th” and “t-th” should be in italic type. We have corrected these in the second-to-last paragraph of Section 3.2.3 (line 457), where the content is as follows: “Where is the weight of the n-th indicator in the t-th month.”
Thank you again for your constructive feedback. We hope these revisions address your concerns, and we believe they contribute to the overall clarity and precision of the manuscript.
Comments 5: Can your methods or models be extended across our country? Why?
Response 5: Thank you for your question regarding the applicability of methods or models in our study. The methodology proposed in this study is can indeed be extended to regions outside of China. Because, the core method, input-output analysis, was developed by Leontief in 1936, originally focusing on the U.S. economy. It captures the economic and technological interconnections between sectors, and input-output tables are now routinely compiled for most countries worldwide. For example, the EORA global supply chain database provides input-output tables for 190 countries. Additionally, databases such as the World Input-Output Database (WIOD), EXIOBASE (a global multi-regional environmental supply-demand table), FIGARO (national and global input-output accounting), and the OECD International Input-Output (OECD-ICIO) tables offer comprehensive data that can be utilized to apply this methodology to other regions. Furthermore, the WAQHD used in this study includes high-frequency pollution emission data for over 100 countries and regions globally, providing a robust foundation for constructing decoupling indicators in other areas. Therefore, the methodology proposed in this study can be applied to other regions, provided regional electricity consumption and price data are available, thus contributing to the existing literature. We further elaborate on this point in the sixth paragraph of the introduction. The revised content is as follows. (Lines in 141-145.)
Finally, the methodology proposed in this study is applicable to regions outside of China with similar characteristics, if relevant data such as regional electricity consumption and price are available. The core input-output analysis method, supported by global databases like EORA, WIOD, and EXIOBASE, can be utilized to extend this approach to other regions, contributing to the broader literature.
Of course, we should acknowledge the limitations that this study only focuses on Fujian province in China and the conditions for other regions to adopt the methods and models proposed in this study. As other reviewers suggest, we extend these in the last paragraph in Section 5. The content is shown below. (Lines in 755-769.)
The decision to focus on Fujian Province in this study stems primarily from the availability of extensive electricity consumption data, which provides a solid foundation for our analysis. Additionally, Fujian’s industrial structure and significance within the broader national context make it a representative case for understanding high-quality growth strategies and decoupling processes. However, this focus on a single province is also a limitation of the study, as the findings may not be fully generalizable to regions with different industrial profiles or energy dynamics. Fortunately, the proposed methodology will be applied to other regions or countries once electricity consumption and price data are available. Future research could expand the geographical scope and incorporate additional variables, such as renewable energy usage, innovation capabilities, and policy interventions, to offer a more holistic understanding of decoupling dynamics across diverse regions. Moreover, investigating the impact of green technologies and government policies on decoupling could provide valuable in-sights into how sustainable economic growth can be further promoted, shedding light on the complex relationship between economic development and environmental sustainability.
Thanks again for your constructive feedback, which helps us improve the clarity and comprehensiveness of our study.
Author Response File: Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsThis paper investigates the relationship between economic growth and air pollution, specifically focusing on how the two can be decoupled. The authors use daily electricity consumption data in Fujian, China, as a proxy for economic activity and explore the environmental implications of energy consumption on air pollution levels.
1. Although the paper uses daily electricity consumption data from Fujian Province as a proxy for economic activity, there is insufficient discussion on data cleaning, validation, and handling of potential biases. Particularly, ensuring the representativeness and accuracy of the data is crucial when performing statistical analyses. For instance, it is unclear whether the electricity consumption data has been seasonally adjusted or subjected to trend analysis.
2. The authors employ econometric models to analyze the relationship between economic growth and air pollution, but the assumptions underlying these models are not adequately discussed. Specifically, there may be endogeneity issues between economic growth and air pollution, which are not addressed in the paper.
3. The paper primarily focuses on a case study from Fujian Province. While this regional study is valuable, there is a lack of cross-regional comparisons of the findings. This may limit the generalizability of the conclusions to other regions.
4. While the literature review mentions several studies on the relationship between economic growth and pollution, it does not adequately cover recent literature, particularly international research on the decoupling concept, which has gained attention in recent years.
5. The paper does not provide a detailed discussion of the limitations of the data and their impact on the results. Specifically, the electricity consumption data from Fujian may be influenced by seasonal variations, policy adjustments, and changes in the energy structure, all of which could affect the robustness of the findings.
Author Response
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Comments 1: Although the paper uses daily electricity consumption data from Fujian Province as a proxy for economic activity, there is insufficient discussion on data cleaning, validation, and handling of potential biases. Particularly, ensuring the representativeness and accuracy of the data is crucial when performing statistical analyses. For instance, it is unclear whether the electricity consumption data has been seasonally adjusted or subjected to trend analysis.
Response 1: Thank you for your valuable comment. We completely agree that testing the accuracy of our models is crucial for ensuring the robustness of the results. In response to your suggestion, we compare the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province to evaluate the accuracy of our models. Both datasets are adjusted to constant prices, and the results are presented in Figure 2. As shown in the last paragraph of Section 3.2.1. The contents are shown below. (Lines 383-394.) In our sample period, global financial crises and the COVID-19 pandemic may have introduced potential biases in the data. To address this, we compare the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province to evaluate the accuracy of our models. Both datasets are adjusted to constant prices, and the results are presented in Figure 2. As Figure 2 shows, the GDP shows a certain degree of seasonality, with GDP increasing slightly from the first to the third quarter, while the fourth quarter sees the highest level of fluctuations. The estimated GDP values follow a similar pattern, with both trends closely aligned. Moreover, the estimated GDP is slightly lower than the actual GDP, with the difference being less than 2%. This consistency further supports the validity of using electricity consumption and direct consumption coefficients to forecast industry value-added trends in Fujian Province.
Figure 2. Comparison of GDP provided by National Bureau of Statistics of China and estimated GDP.
Although the electricity consumption data used in calculating regional GDP is not seasonally adjusted and may be subject to trend fluctuations, we have extracted the long-term trend components of each indicator using the Hodrick-Prescott filter method. We apologize for not mentioning this adjustment in the initial draft. This has been included in the revised manuscript. Details can be found on lines 448-451. Considering the seasonality of electricity consumption data, we use the Hodrick–Prescott filter to extract the long-term trend items for each indicator. Since there are only 54 observations in each prefecture-level city, the smooth parameter is set to 4. The below is based on these extracted trend items. We hope these additional tests provide the necessary evidence to support the accuracy and robustness of our models. Thank you again for your insightful suggestion, which allows us to further clarify the methods and justify our approach to handling data and forecasting.
Comments 2: The authors employ econometric models to analyze the relationship between economic growth and air pollution, but the assumptions underlying these models are not adequately discussed. Specifically, there may be endogeneity issues between economic growth and air pollution, which are not addressed in the paper.
Response 2: Thank you for your constructive comments. We appreciate your suggestion regarding the discussion of the assumptions underlying the econometric models used in the study. In response, we acknowledge the potential endogeneity issues between economic growth and air pollution, which could arise due to omitted variables and reverse causality. To address these concerns, we incorporate the lag term of the HQDI and the average HQDI of other cities (excluding the focal city) as instrumental variables. This approach helps mitigate the potential endogeneity from omitted variables and reverse causality. We add the discussion of the econometric model and the adoption of instrumental variables in a dedicated Section 4.3 Instrumental Variable Analysis of the Impact of HQDI on the Decoupling of Economic Growth from Air Pollution. Detailed analysis and results are presented in this section. (Lines 571-604.) As we found, HQDI can increase the likelihood that cities will achieve a decoupling state. However, it is necessary to discuss the validity of the adopted econometric model. The econometric model presented in this paper (Equation 8) may be subject to two potential issues. First, due to difficulties obtaining specific monthly economic indicators for prefecture-level cities, such as energy consumption structure, industrial structure, and policy variables, both the HQDI and decoupling effects could be influenced simultaneously. As a result, endogeneity problems may arise from the omitted variables. To address this, we control for both year and city fixed effects, which helps mitigate the impact of omitted variable bias on our estimation results. Second, while HQDI influences the decoupling effect, the decoupling effect itself captures an optimal state where economic growth occurs alongside reduced pollution emissions. This relationship may, in turn, affect HQDI, potentially leading to reverse causality and endogeneity concerns. To mitigate these concerns, we adopt instrumental variables, specifically the lagged term of the HQDI and the average HQDI of cities other than the focal city, as commonly practiced in literature. The rationale for utilizing two instrumental variables is that when there is only one endogenous variable, the exogeneity of the instrumental variables can be tested using Hansen J statistic. The results of the instrumental variable regression are presented in Table 5. The results in Table 5 suggest that the two instrumental variables significantly affect HQDI in the first stage at the 1% confidence level. Moreover, the Kleibergen–Paap rk Wald F statistic is 102.229, well above 10, indicating no issue of a weak instrumental variable. In the second stage, the P-value of the Hansen J statistic is 0.388, indicating that we cannot reject the null hypothesis of exogeneity for all instrumental variables, confirming their relevance and exogeneity. Regarding the regression coefficients, the impact of HQDI on decoupling from PM2.5 remains significantly positive at the 1% level, confirming the robustness of our results. The HQDI coefficient of 0.479 is 1.86 times higher than the benchmark regression value of 0.258, suggesting that potential endogeneity may lead to an underestimation of HQDI’s effect on decoupling. This underestimation is within an acceptable range, further supporting the robustness of our findings. We believe that the instrumental variables incorporated in this study effectively solve the endogeneity problem that may exist in the model. Thanks again for your insightful comments and suggestions, which greatly improve the clarity and quality of our paper.
Comments 3: The paper primarily focuses on a case study from Fujian Province. While this regional study is valuable, there is a lack of cross-regional comparisons of the findings. This may limit the generalizability of the conclusions to other regions.
Response 3: Thank you for your valuable comment. We agree that cross-regional comparisons can enhance the generalizability of our findings and provide deeper insights into the broader applicability of the methodology. However, due to the limitations of data, this study only focuses on Fujian province in China. However, the methodology proposed in this study can be extended to regions outside of China. Because, the core method, input-output analysis, was developed by Leontief in 1936, originally focusing on the U.S. economy. It captures the economic and technological interconnections between sectors, and input-output tables are now routinely compiled for most countries worldwide. As other reviewers suggest, we extend the discussion of our limitations and the conditions for other regions to adopt the methods and models proposed in this study in the last paragraph in Section 5. The content is shown below. (Lines in 755-769.) The decision to focus on Fujian Province in this study stems primarily from the availability of extensive electricity consumption data, which provides a solid foundation for our analysis. Additionally, Fujian’s industrial structure and significance within the broader national context make it a representative case for understanding high-quality growth strategies and decoupling processes. However, this focus on a single province is also a limitation of the study, as the findings may not be fully generalizable to regions with different industrial profiles or energy dynamics. Fortunately, the proposed methodology will be applied to other regions or countries once electricity consumption and price data are available. Future research could expand the geographical scope and incorporate additional variables, such as renewable energy usage, innovation capabilities, and policy interventions, to offer a more holistic understanding of decoupling dynamics across diverse regions. Moreover, investigating the impact of green technologies and government policies on decoupling could provide valuable in-sights into how sustainable economic growth can be further promoted, shedding light on the complex relationship between economic development and environmental sustainability. Thank you once again for your constructive feedback, which has been instrumental in improving the clarity and comprehensiveness of our study.
Comments 4: While the literature review mentions several studies on the relationship between economic growth and pollution, it does not adequately cover recent literature, particularly international research on the decoupling concept, which has gained attention in recent years.
Response 4: Thank you for your valuable feedback. In response, we update the literature review by incorporating more recent studies on the decoupling concept. Specifically, we add references to Lazăr et al. (2019) and Mughal et al. (2022) in the last two sentences of the first paragraph in Section 4.1 to highlight existing literature on the relationship between economic growth and pollution. Details as below. (Lines 170-176.) However, Lazăr et al. (2019) [10] identify a complex, nonlinear relationship between GDP and CO2 emissions in Central and Eastern European countries, which can take various forms, including N-shaped, inverted-N, U-shaped, inverted-U, monotonic, or even no statistical link. Mughal et al. (2022) [11] further confirm there is an inverted-U-shaped Environmental Kuznets Curve (EKC) relationship between economic growth and CO2 emissions in selected South Asian economies. Additionally, we include recent studies such as Engo (2018), Khan and Majeed (2023), and Riveros and Shahbaz (2024), which apply the Tapio model to examine the decoupling effect. These additions ensure the literature review covers both the economic-pollution relationship and the growing international attention on the decoupling concept. Details as below. For instance, Engo (2018) [17] utilizes the Tapio and LMDI methods, based on an extended Kaya identity, to assess the decoupling of economic growth and CO2 emissions in Cameroon from 1990 to 2015. (Lines 203-205.) Khan and Majeed (2023) [21] analyze Pakistan’s decoupling from 1980 to 2018, finding Expensive Negative Decoupling (END) driven by carbon intensity and urbanization. (Lines 213-215.) Riveros and Shahbaz (2024) [23] apply the TAPIO model, Kaya identity, and LMDI to study Colombia’s decoupling from 1975 to 2021, focusing on economic structure and energy consumption. (Lines 217-219.) We hope that these changes address your comment and further enhance the comprehensiveness of literature review. Thank you again for your constructive suggestion.
Comments 5: The paper does not provide a detailed discussion of the limitations of the data and their impact on the results. Specifically, the electricity consumption data from Fujian may be influenced by seasonal variations, policy adjustments, and changes in the energy structure, all of which could affect the robustness of the findings.
Response 5: Thank you for your valuable comment. We agree that a detailed discussion of the data limitations is important for a comprehensive understanding of the results. Regarding the electricity consumption data from Fujian, we acknowledge that seasonal variations, policy adjustments, and changes in the energy structure could indeed influence the findings. While the electricity consumption data is not seasonally adjusted, we use the monthly year-on-year growth rates of output and emissions. This approach helps alleviate some of the seasonal effects by focusing on relative changes rather than absolute values. As for the seasonality of electricity consumption data, we extract the long-term trend items of each indicator by utilizing the Hodrick–Prescott filter method. Details are shown as follows. (Lines 448-451.) Considering the seasonality of electricity consumption data, we use the Hodrick–Prescott filter to extract the long-term trend items for each indicator. Since there are only 54 observations in each prefecture-level city, the smooth parameter is set to 4. The below is based on these extracted trend items. We also recognize the potential impact of policy changes and the evolving energy mix in the region. However, fully quantifying these factors in this study is challenging due to data limitations. To mitigate the impact of such omitted variables, we control for city and year fixed effects, which helps account for time-invariant unobserved factors at the city level. Additionally, in the revised manuscript, we incorporate the lag term of HQDI and the average HQDI of other cities apart from the focal city as instrumental variables to address potential endogeneity issues. The reason for choosing two instrumental variables is that when there is only one endogenous variable, the exogeneity of the instrumental variable can be tested by Hansen J statistics. This adjustment enhances the validity and robustness of our results. Furthermore, we perform various sensitivity tests to demonstrate the stability of our results despite the data limitations, such as replacing dependent variables, changing estimation methods, adjusting control variables, and sensitivity analysis of extreme high temperature. All the results verify the robustness of our conclusions, as shown in Section 4.3 and 4.4 in the revised manuscript. Details as below. (Lines 572-662.) 4.3 Instrumental variable analysis of the impact of HQDI on the decoupling of economic growth from air pollution As we found, HQDI can increase the likelihood that cities will achieve a decoupling state. However, it is necessary to discuss the validity of the adopted econometric model. The econometric model presented in this paper (Equation 8) may be subject to two potential issues. First, due to difficulties obtaining specific monthly economic indicators for prefecture-level cities, such as energy consumption structure, industrial structure, and policy variables, both the HQDI and decoupling effects could be influenced simultaneously. As a result, endogeneity problems may arise from the omitted variables. To address this, we control for both year and city fixed effects, which helps mitigate the impact of omitted variable bias on our estimation results. Second, while HQDI influences the decoupling effect, the decoupling effect itself captures an optimal state where economic growth occurs alongside reduced pollution emissions. This relationship may, in turn, affect HQDI, potentially leading to reverse causality and endogeneity concerns. To mitigate these concerns, we adopt instrumental variables, specifically the lagged term of the HQDI and the average HQDI of cities other than the focal city, as commonly practiced in literature. The rationale for utilizing two instrumental variables is that when there is only one endogenous variable, the exogeneity of the instrumental variables can be tested using Hansen J statistic. The results of the instrumental variable regression are presented in Table 5. The results in Table 5 suggest that the two instrumental variables significantly affect HQDI in the first stage at the 1% confidence level. Moreover, the Kleibergen–Paap rk Wald F statistic is 102.229, well above 10, indicating no issue of a weak instrumental variable. In the second stage, the P-value of the Hansen J statistic is 0.388, indicating that we cannot reject the null hypothesis of exogeneity for all instrumental variables, confirming their relevance and exogeneity. Regarding the regression coefficients, the impact of HQDI on decoupling from PM2.5 remains significantly positive at the 1% level, confirming the robustness of our results. The HQDI coefficient of 0.479 is 1.86 times higher than the benchmark regression value of 0.258, suggesting that potential endogeneity may lead to an underestimation of HQDI’s effect on decoupling. This underestimation is within an acceptable range, further supporting the robustness of our findings.
Table 5. Results of instrumental variable regression.
4.4 Robustness test of the impact of HQDI on the decoupling economic growth from air pollution 4.4.1 Robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification To verify the robustness of the benchmark results, this study conducts several robustness tests, including replacing the explained variable, involving outlier removal, adjusting the econometric model, and modifying the control variables. First, we redefine the decoupling from SO2 and decoupling from PM10 to reveal whether the impact of HQDI on the decoupling effect from different pollutants is still held. The results are shown in columns (1) and (2) of Table 6. Second, to ensure the prediction method does not bias the results, Zhangzhou is excluded from the analysis due to significant gaps in its pollution emission data, which are estimated using data from other cities and local temperature information in the baseline analysis; the results are reported in column (3) of Table 6. Third, given that the explanatory variables are binary indicators of strong or weak decoupling between economic growth and pollution emissions, a probit model is adopted to assess whether the findings are sensitive to the choice of regression model. The results of this analysis are presented in column (4) of Table 6. Then, the measurement of industry concentration is replaced with the market share percentage of the top four industries to provide a more precise representation of regional industry concentration, as shown in column (5) of Table 6. Finally, we add nonlinear research into HQDI on the decoupling effect, as shown in column (6) of Table 6. The results in the first two columns in Table 6 suggest that HQDI positively impacts the decoupling from SO2 and PM10 at the 1% confidence level, confirming the robustness of HQDI on the decoupling effect from different pollutants. Moreover, the magnitude of coefficients reveals that a 1% increase in HQDI can increase the probabilities of decoupling from SO2 and PM10 by 44% and 42%, respectively. These findings underscore the role of HQDI in promoting sustainable development driven by innovation, green development, and technological progress. Fujian Province has actively embraced high-quality development principles, adjusting its industrial and energy structures to support decoupling efforts. Moreover, the results in columns (3)–(5) in Table 6 indicate that HQDI has a significant positive impact on the decoupling effect of PM2.5 at the 1% confidence level, thereby validating the reliability of the benchmark findings.
Table 6. Results of robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification.
4.4.2 Robustness test: Sensitivity analysis of extreme high temperature In the benchmark regression, we define extreme high-temperature days as the total number of days per month with a maximum temperature of 30°C or higher. This definition assumes that when temperatures exceed 30℃, industrial equipment consumes additional energy for cooling, leading to higher overall energy consumption, which could influence the decoupling effect. The results indeed show that an increase in extreme high-temperature days reduces the decoupling effect between economic growth and pollution emissions. However, due to the difficulty in defining a precise threshold for extreme high temperatures, we conduct a robustness check by redefining extreme high-temperature days as those between 28°C and 35°C. The results are presented in Table 7. The results of this robustness test, shown in Table 7, reveal that even with this modified definition, HQDI continues to significantly increase the decoupling effect. Additionally, the negative impacts of extreme high-temperature days and the Herfindahl–Hirschman Index on decoupling remain statistically significant, consistent with the benchmark results. This sensitivity analysis indicates that the conclusions drawn in this study are robust and not sensitive to the specific choice of extreme high-temperature threshold. Regardless of how extreme high-temperature days are defined, HQDI has a robust and positive effect on decoupling, while extreme temperatures remain a significant factor hindering the decoupling of economic growth and pollution emissions.
Table 7. Results of robustness test: Sensitivity analysis of extreme high temperature.
We appreciate your suggestion and will include a more detailed discussion of these limitations and their potential impact on the robustness of our findings in the revised manuscript. |
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Author Response File: Author Response.docx
Reviewer 4 Report
Comments and Suggestions for AuthorsGeneral Comments
The manuscript explores the influence of high-quality development on the decoupling of economic growth from pollution emissions, utilizing data from nine cities in Fujian Province. It provides valuable insights and employs innovative methodologies to address an important and relevant topic. As such, the presented research is both relevant and interesting for a larger audience. However, some sections would benefit from clearer explanations, better organization, and a deeper exploration of the implications of findings.
Abstract
· The abstract effectively summarizes the study but could be more concise. For example, the description of methods and key findings is overly detailed for this section.
- I would consider rephrasing sentences to highlight the novelty of the study more explicitly. Several sentences on the technical aspects could be shortened if the word count is limited.
- It would help to include 1-2 specific policy recommendations briefly in the abstract. This could help draw in additional readers by showcasing the relevance of the research to a wider audience.
Introduction
- The introduction provides a solid context but could improve by more explicitly defining the research gap with some additional references (outside of Chen’s work). While prior studies on decoupling are mentioned, it’s unclear how this paper adds novel insights to existing literature.
- A stronger transition is needed between the discussion of national policies and the specific focus on Fujian Province. Why exactly does Fujian serve as a representative case for high-quality development and decoupling research?
- Are there other regions similar to Fujian Province outside of China where the proposed methodology can be validated?
Literature Review
- The review summarizes key concepts like HQDI and decoupling well. However, it lacks engagement with prior research. For instance, are there contradictions or gaps in existing literature on decoupling using Tapio’s model? Currently it appears that the Tapio model has no drawbacks.
- Some citations feel a bit outdated (e.g., references from the 1990s or early 2000s). Updating with more recent sources would ensure the relevance of the review.
Methodology
- This section is very thorough so readers can follow along, but it is quite dense to read. Some subsections are technical and may not be accessible to all readers. Including a step-by-step flowchart or visual representation could improve understanding.
- The use of electricity data and input-output analysis is innovative, but the interpolation and AR models for coefficients (2018–2024) require more justification. Why were these methods chosen over others? Are there potential biases in the results due to these assumptions (financial crises, COVID lockdown periods,…)?
- The variable definitions in Table 1 are clear and helpful, but the rationale for choosing the threshold values for the control variables such as extreme high temperature could be elaborated further. Was a sensitivity analysis performed on the threshold values of these control variables, e.g., temperature cut-off of 30°C seems arbitrary?
Results and Discussion
- The results are presented clearly, but more emphasis is needed on interpreting the practical significance of the findings. For example: How significant is a 26% increase in the probability of decoupling for policymakers or industry stakeholders? What are the practical or policy implications of this?
- Figures and tables could be better integrated into the text. For example, Figure 1 (distribution of decoupling statuses) is discussed only briefly, but it provides important context for understanding regional disparities.
- The robustness checks are a strength of the paper but could be summarized more effectively. Currently, they appear scattered across the section.
Conclusion
- The conclusion effectively summarizes key findings but misses an opportunity to provide actionable insights. For example:
- What specific policies or programs should Fujian cities implement to further strengthen decoupling, especially in industries where brown industries dominate? Do you foresee this relationship breaking down at a certain point?
- Could the HQDI framework be adopted by other provinces or countries? If so, what adaptations might be necessary? It would be useful to present a table with values (such as the extreme temperature threshold) which would probably need to be changed for different climates.
- The study’s limitations are understated. For instance, acknowledging the potential inaccuracies in estimated electricity consumption coefficients or the limited geographic focus would make the discussion more balanced.
- Suggest explicitly outlining directions for future research, such as the integration of renewable energy data or a broader cross-provincial analysis.
Figures, Tables, and Visuals
- Most tables are informative. However, Table 3 is quite dense to follow. I do not have an immediate suggestion for improvement, but maybe the authors can reflect on possible alternatives to present this data.
- I would suggest adding the explanation of different abbreviations in the caption of the figures and tables where necessary. For example, the legend in Figure 2 contains abbreviations. It would be helpful to include the full terms in the caption of this figure for readability. Similar for Table 4, write DDEC in full.
I would suggest thorough editing of this manuscript for the English language. Specific issues to watch out for:
- The manuscript tends to use unnecessarily complex and technical phrases, especially in longer sentences. Simplifying language and breaking longer sentences into shorter, more direct sentences (without losing meaning) would make the text more accessible.
- Be mindful for subject-verb agreement errors, especially in longer sentences.
- Make sure terminology is used consistently throughout the manuscript.
- Make sure to use straightforward language in both the abstract and conclusion to highlight both the novelty of the work, as well as the practical implications (for industry as well as policy makers).
Author Response
Comments 1: (General Comments): The manuscript explores the influence of high-quality development on the decoupling of economic growth from pollution emissions, utilizing data from nine cities in Fujian Province. It provides valuable insights and employs innovative methodologies to address an important and relevant topic. As such, the presented research is both relevant and interesting for a larger audience. However, some sections would benefit from clearer explanations, better organization, and a deeper exploration of the implications of findings.
Response 1: Thank you for your insightful feedback and the opportunity to enhance the clarity and organization of certain sections, as well as to further explore the implications of our findings. We appreciate your recognition of the relevance and innovation of our research. In response to your comments, we carefully review each comment to improve the overall structure and ensure that the key findings are better articulated, and their implications more thoroughly discussed.
We hope that these improvements will strengthen the paper’s contribution to the field and make the insights more accessible to a broader audience. Thank you once again for your valuable feedback. We look forward to any further suggestions you might have and are eager to improve our manuscript to meet the journal’s standards.
Comments 2: (Abstract): The abstract effectively summarizes the study but could be more concise. For example, the description of methods and key findings is overly detailed for this section.
I would consider rephrasing sentences to highlight the novelty of the study more explicitly. Several sentences on the technical aspects could be shortened if the word count is limited.
It would help to include 1-2 specific policy recommendations briefly in the abstract. This could help draw in additional readers by showcasing the relevance of the research to a wider audience.
Response 2: Thank you for your valuable feedback. We appreciate your suggestions to make the abstract more concise and to highlight the novelty of the study more explicitly. In response to your comments, we revise the abstract to shorten the technical descriptions while emphasizing the unique contributions of our research. Additionally, we incorporate 1-2 specific policy recommendations to further demonstrate the practical relevance of our findings and attract a broader audience. We believe these changes will improve the clarity and impact of the abstract. See details in Section Abstract. The content is as follows. (Lines in 12-25.)
In the context of growing challenges associated with pollution prevention and control, developing more efficient technologies and precise policy measures to address the bottleneck period is imperative. This study utilizes daily electricity consumption data from nine prefecture-level cities in Fujian Province from January 2019 to June 2024 to develop a high-quality development index (HQDI) and empirically investigate how HQDI affects the decoupling of economic growth from pollution emissions. Results suggest that HQDI can significantly promote decoupling, with innovation, openness, and sharing playing positive roles, while brown industries development and the electricity capacity installation of small and micro-enterprises hinder these processes. Moreover, extreme high temperatures exert a significant negative impact on decoupling, whereas increased market concentration fosters decoupling. Policy recommendations include prioritizing innovation, green technologies, and energy efficiency (particularly for SMEs), addressing climate resilience, and expanding HQDI to include factors like digital technologies for sustainable growth in Fujian and similar regions.
We believe that these contributions significantly enhance the understanding of our study. Thanks again for your valuable feedback.
Comments 3: (Introduction): The introduction provides a solid context but could improve by more explicitly defining the research gap with some additional references (outside of Chen’s work). While prior studies on decoupling are mentioned, it’s unclear how this paper adds novel insights to existing literature.
Response 3: Thank you for your insightful feedback and the opportunity to clarify the innovative aspects of our study. In response to your comments, we incorporate a more detailed discussion of the research gap by citing relevant studies beyond Chen’s work to better highlight how our study contributes novel insights to the existing decoupling literature. See details in paragraphs 6-10 in Section 1. The content is as follows. (Lines 112-145.)
This study makes several significant contributions, as outlined below. First, to address the dearth of monthly industry data, this study employs an innovative methodology that combines input–output tables and big data on electricity to estimate regional output and the scale of the digital economy. On the one hand, the input–output table elucidates the technical and economic interconnections between sectors. Conversely, as a “barometer” of economic activity, the big data on electric power directly correlates with both economic development and environmental sustainability. These data can reflect economic dynamics with greater precision and in real time, thereby compensating for the limitations of traditional data and enhancing insights into the dynamics of economic structure changes.
Secondly, by incorporating electricity consumption data, we can capture not only traditional economic growth indicators but also measure the activity of small and micro-enterprises, the structure of industries, the development of high-tech sectors, and the scale of digital industrialization. These factors are not typically available through conventional macroeconomic indicators and offer a more comprehensive view of high-quality development and innovation. Thus, these factors enhance our ability to understand the decoupling relationship between economic growth and pollution, which may be overlooked when relying solely on traditional measures such as GDP.
Thirdly, given that most research focuses on traditional macroeconomic factors, such as energy consumption intensity, industrial structure, and population size, this study explores the dynamic relationship between economic growth and the decoupling of pollution emissions by refining industry-level data. The monthly year-on-year growth of pollution emissions and GDP is calculated using daily electricity consumption data and the Tapio model, eliminating seasonality and economic cycle effects and accurately capturing the influence of changes in industrial structure on the decoupling effect.
Fourthly, given the representative nature of Fujian Province with respect to economic and environmental governance, this study investigates the impact of HQDI on the decoupling effect at the prefecture level. This analysis reveals how high-quality development promotes the coordination of economy and environment, providing theoretical support and empirical evidence for other regions to achieve sustainable economic development.
Finally, the methodology proposed in this study applies to regions outside China with similar characteristics if relevant data such as regional electricity consumption and price are available. The core input–output analysis method, supported by global databases like EORA, WIOD, and EXIOBASE, can be utilized to extend this approach to other regions, contributing to the broader literature.
We believe that these contributions significantly enhance the understanding of the complex relationship between economic growth and pollution, providing a more comprehensive perspective than existing studies. Thanks again for your valuable feedback.
Comments 4: (Introduction): A stronger transition is needed between the discussion of national policies and the specific focus on Fujian Province. Why exactly does Fujian serve as a representative case for high-quality development and decoupling research?
Response 4: Thank you for your valuable feedback. Regarding the transition from national policies to the specific focus on Fujian Province, we agree that a clearer link is needed. We strengthen this transition by elaborating on why Fujian serves as a representative case for high-quality development and decoupling research. Specifically, we will emphasize Fujian’s unique characteristics, such as its economic structure, policy environment, and the challenges it faces in achieving decoupling, which make it an ideal region for this study. See details in paragraphs 2 and 3 in Section 1. The content is as follows. (Lines in 45-73.)
Fujian, a pivotal province situated along China’s southeastern coast, is a prime example of China’s broader national policies aimed at promoting high-quality development and achieving the decoupling of economic growth from environmental degradation. In 2023, Fujian’s GDP reached 5.44 trillion yuan, constituting 4.31% of the national total. The province has undergone a significant transformation from a manufacturing-based economy to a more service-oriented structure, with the tertiary sector now accounting for 49.99%, followed by the secondary sector at 44.09%, and the primary sector at 5.92%. This structural shift aligns with China’s broader goal of transitioning to a more sustainable, consumption-driven economy. Fujian’s strategic positioning within the “Belt and Road Initiative” and the Maritime Silk Road has fostered the development of traditional industries, such as electronics and machinery, and emerging sectors like information technology and environmental protection. This economic evolution is directly influenced by national policies promoting innovation, technological advancement, and sustainable development. Furthermore, Fujian has consistently demonstrated its commitment to environmental sustainability, achieving a forest coverage rate of 65.12% for 45 consecutive years, aligning with national environmental goals.
Moreover, Fujian’s proactive approach to environmental sustainability reflects the central government’s push for cleaner, greener industrial practices. Notably, the province has implemented ultra-low emission measures, including the phase-out of 90% of small coal-fired boilers, to improve air quality. These efforts have had significant results, with 98.5% of days across nine cities classified as having good air quality. This is a direct manifestation of the high-quality development strategy promoted by national policies prioritizing a balance between economic growth and environmental protection. Through its combination of economic transition, technological innovation, and environmental commitment, Fujian serves as a representative case for the broader trends and challenges of high-quality development and decoupling in China. The province’s ability to harmonize economic and ecological goals makes it an ideal region for examining the implementation and impacts of national policies, thus providing valuable insights into the feasibility and challenges of achieving regional-level sustainable development.
Thank you again for your insightful comments. We believe these clarifications will provide a stronger link between the national policy context and the case study of Fujian, enhancing the coherence of the introduction.
Comments 5: (Introduction): Are there other regions similar to Fujian Province outside of China where the proposed methodology can be validated?
Response 5: Thank you for your insightful comment. The methodology proposed in this study is indeed applicable to regions outside of China that share similar characteristics with Fujian Province, even other regions once the data quality is available. The core method, input-output analysis, was developed by Leontief in 1936, originally focusing on the U.S. economy. It captures the economic and technological interconnections between sectors, and input-output tables are now routinely compiled for most countries worldwide. For example, the EORA global supply chain database provides input-output tables for 190 countries. Additionally, databases such as the World Input-Output Database (WIOD), EXIOBASE (a global multi-regional environmental supply-demand table), FIGARO (national and global input-output accounting), and the OECD International Input-Output (OECD-ICIO) tables offer comprehensive data that can be utilized to apply this methodology to other regions. Furthermore, the WAQHD used in this study includes high-frequency pollution emission data for over 100 countries and regions globally, providing a robust foundation for constructing decoupling indicators in other areas. Therefore, the methodology proposed in this study can be applied to other regions, provided regional electricity consumption and price data are available, thus contributing to the existing literature. We further elaborate on this point in the sixth paragraph of the introduction. The revised content is as follows. (Lines in 141-145.)
Finally, the methodology proposed in this study is applicable to regions outside of China with similar characteristics, if relevant data such as regional electricity consumption and price are available. The core input-output analysis method, supported by global databases like EORA, WIOD, and EXIOBASE, can be utilized to extend this approach to other regions, contributing to the broader literature.
Comments 6: (Literature Review): The review summarizes key concepts like HQDI and decoupling well. However, it lacks engagement with prior research. For instance, are there contradictions or gaps in existing literature on decoupling using Tapio’s model? Currently it appears that the Tapio model has no drawbacks.
Response 6: Thank you for your constructive feedback on the literature review. We appreciate your observation regarding the lack of engagement with prior research and the potential limitations of Tapio’s model. In response, we enhance the review by discussing contradictions and gaps in existing literature on decoupling. While the Tapio model has been widely applied in decoupling studies, there are critiques concerning its applicability in specific contexts and its limitations in capturing the full complexity of decoupling dynamics. We address these points to provide a more balanced view of the model’s strengths and weaknesses. The revised content is presented in the fifth paragraph of Section 2.2, as follows. (Lines in 224-232.)
The Tapio model effectively characterizes the relationship between economic growth and pollution emissions, particularly by categorizing different decoupling states. However, it is limited to state analysis and does not capture the causal relationship between economic growth and emissions. To overcome this, it should be combined with other models for a more comprehensive analysis. Additionally, the model is highly reliant on data accuracy and completeness. Many countries and regions face challenges related to incomplete or inaccurate data collection, which can impact the model’s reliability. Furthermore, the applicability of the Tapio model may vary depending on regional differences in data quality and availability, which can restrict its use in diverse contexts.
Thank you again for your valuable feedback. We hope these revisions provide a clearer, more nuanced understanding of the Tapio model’s role and limitations in decoupling research.
Comments 7: (Literature Review): Some citations feel a bit outdated (e.g., references from the 1990s or early 2000s). Updating with more recent sources would ensure the relevance of the review.
Response 7: Thank you for your helpful comment. We acknowledge your comment about the outdated citations. In response, we update the literature review by replacing outdated references with more recent sources. This ensures that the citations reflect the latest developments in the field and maintain the relevance of the review. We provide a detailed summary of the changes made, as shown below.
Chang and Robin (2008): The reference "12. Chang, C. L.; Robin, S., Public policy, innovation and total factor productivity: An application to Taiwan’s manufacturing industry. Math. Comput. Simul. 2008, 79, (3), 352-367" has been replaced with "32. Rehman, F. U., Islam, M. M. Financial infrastructure——total factor productivity (TFP) nexus within the purview of FDI outflow, trade openness, innovation, human capital and institutional quality: Evidence from BRICS economies. Appl. Econ., 2023, 55(7), 783-801." We cite Chang and Robin (2008) to demonstrate that technological innovation enhances economic productivity by improving total factor productivity (TFP). In a similar vein, Rehman and Islam (2023) explore the relationship between TFP and key factors such as foreign direct investment (FDI), trade openness, innovation, human capital, and institutional quality in the BRICS economies over the period from 1990 to 2019. Their findings indicate that technological factors are crucial in driving economic growth. We believe that this more recent study aligns well with the message of Chang and Robin (2008) and provides a timely update to the literature. (Lines 856-858)
Stern (2004): The reference "18. Stern, D. I., The rise and fall of the environmental Kuznets curve. World Dev. 2004, 32, (8), 1419-1439." has been deleted and replaced with "8. Wen, J., Mughal, N., Zhao, J., Shabbir, M. S., Niedbała, G., Jain, V., Anwar, A. Does globalization matter for environmental degradation? Nexus among energy consumption, economic growth, and carbon dioxide emission. Energy Policy, 2021, 153, 112230." Wen et al. (2021) show that as economic growth increases, environmental pollution also rises. However, once economic growth surpasses a certain threshold, environmental degradation starts to decline. This finding aligns with the Environmental Kuznets Curve (EKC) theory proposed by Stern (2004), and we believe that the updated results in Wen et al. (2021) provide a more current and relevant perspective for this discussion. (Lines 806-808)
Freitas and Kaneko (2011): The reference "24. De Freitas, L. C.; Kaneko, S., Decomposing the decoupling of CO2 emissions and economic growth in Brazil. Ecol. Econ. 2011, 70, (8), 1459-1469." has been replaced with "24. Yang, J., Hao, Y., Feng, C. A race between economic growth and carbon emissions: What plays important roles towards global low-carbon development?. Energy Econ., 2021, 100, 105327." We cite Freitas and Kaneko (2011) to demonstrate the application of the LMDI (Logarithmic Mean Divisia Index) combined with the Tapio model. In a similar manner, Yang et al. (2021) employ both the Tapio method and LMDI to investigate the differences in decoupling indices across six continents and major regions from 2001 to 2017. They also explore the key drivers of decoupling and their contributions to the decoupling index. As such, we have updated the citation to Yang et al. (2021) and made the following revision in the text: " Yang et al. (2021) [19] combine the Tapio method and LMDI to investigate the differences in the decoupling index and explore the key drivers of decoupling and their contributions to the decoupling index across six continents and major regions during the period 2001-2017." (Lines 830-831 and 208-211).
Tapio (2005) and Grossman and Krueger (1995): As for Tapio (2005) and Grossman and Krueger (1995) were pioneers in proposing the Tapio theory and the Environmental Kuznets Curve (EKC) theory, respectively, and provided the foundational theoretical support for subsequent research in this area, we believe it is important to retain these two references. Their contributions remain indispensable to the theoretical framework of this study, and we keep them.
Thanks again for your valuable comment. We appreciate your suggestion, and we believe these updates enhance the timeliness and relevance of the review.
Comments 8: (Methodology): This section is very thorough so readers can follow along, but it is quite dense to read. Some subsections are technical and may not be accessible to all readers. Including a step-by-step flowchart or visual representation could improve understanding.
Response 8: Thank you for your insightful feedback. We appreciate your suggestion to improve the accessibility of the methodology section. To enhance clarity and make the more technical aspects easier to follow, we include a step-by-step flowchart representation that illustrates the methodology. This addition aims to simplify complex concepts and provide readers with a clearer understanding of the process. The updated flowchart can be found in Figure 3 in Section 3. (Lines 501-504.)
The technical roadmap including step-by-step indicator construction and analysis methods is shown in Figure 3.
Figure 3. The technical roadmap includes step-by-step indicator construction and analysis methods.
We believe this visual aid will improve the overall readability of the section and ensure that even non-expert readers can follow the methodology with ease. Thank you again for your valuable suggestion.
Comments 9: (Methodology): The use of electricity data and input-output analysis is innovative, but the interpolation and AR models for coefficients (2018-2024) require more justification. Why were these methods chosen over others? Are there potential biases in the results due to these assumptions (financial crises, COVID lockdown periods, …)?
Response 9: Thank you for your constructive comment. We appreciate your recognition of the innovation in our use of electricity data and input-output analysis. Regarding the interpolation and AR models used for estimating coefficients from 2018 to 2024, we select these methods based on their ability to provide reasonable forecasts. For one thing, the interpolation method was chosen due to its ability to estimate missing data points within a known range. Pan et al. (2011) employ linear interpolation for handling missing data when adopt the input-output table of China to measure the industry similarity. Their approach suggests that industry similarity is highest between adjacent years, with the similarity decreasing as the gap between years increases. This reasoning underpinned our choice of linear interpolation for this study.
For another, the adoption of Autoregressive (AR) models to predict the direct electricity consumption coefficients for 2018-2024 is based on the time-series nature of these coefficients. AR models are particularly effective in capturing temporal dependencies and trends in historical data, allowing us to make more informed predictions about future patterns. We provide further explanation of our data processing methods in Section 3.2.1, as detailed below (Lines 362-367.)
In the absence of a regional input–output table for non-tabulated years, we apply linear interpolation to estimate missing coefficients between 2002 and 2017, following the approach of Pan et al. (2011) [40]. Given that AR models are highly effective in capturing temporal dependencies, we adopt this model to the historical coefficients from 2002 to 2017 to predict the coefficients for 2018-2024.
As you rightly point out, the global financial crisis and the COVID-19 pandemic may have introduced potential biases into the data. To address this concern, we compared the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province, both adjusted to constant prices. The results of this comparison are presented in Figure 2. As shown in the last paragraph of Section 3.2.1.
(Lines 382-395.)
In our sample period, global financial crises and the COVID-19 pandemic may have introduced potential biases in the data. To address this, we compare the GDP data from the National Bureau of Statistics of China with our estimated GDP for Fujian Province to evaluate the accuracy of our models. Both datasets are adjusted to constant prices, and the results are presented in Figure 2. As Figure 2 shows, the GDP shows a certain degree of seasonality, with GDP increasing slightly from the first to the third quarter, while the fourth quarter sees the highest level of fluctuations. The estimated GDP values follow a similar pattern, with both trends closely aligned. Moreover, the estimated GDP is slightly lower than the actual GDP, with the difference being less than 2%. This consistency further supports the validity of using electricity consumption and direct consumption coefficients to forecast industry value-added trends in Fujian Province.
Figure 2. Comparison of GDP provided by the National Bureau of Statistics of China and estimated GDP.
Thank you once again for your insightful feedback, which allows us to further clarify the methods and justify our approach to handling data and forecasting.
Comments 10: (Methodology): The variable definitions in Table 1 are clear and helpful, but the rationale for choosing the threshold values for the control variables such as extreme high temperature could be elaborated further. Was a sensitivity analysis performed on the threshold values of these control variables, e.g., temperature cut-off of 30°C seems arbitrary?
Response 10: Thank you for your insightful comment. We agree that further elaboration on the rationale for choosing the threshold values for control variables, such as extreme high temperature, is important. In the benchmark regression, we define extreme high-temperature days as the total number of days per month with a maximum temperature of 30°C or higher. This threshold is selected because temperatures exceeding 30°C often lead to increased energy consumption for industrial cooling, which in turn may affect the decoupling effect between economic growth and pollution emissions. However, recognizing the potential arbitrariness of the 30°C threshold, we conducted a sensitivity analysis to test the robustness of our results. Specifically, we redefined extreme high-temperature days to include those with temperatures ranging from 28°C to 35°C. The results of this robustness check are shown in Table 7. The explanations are presented in the last paragraph of Section 4.2, details as below. (Lines 641-661.)
In the benchmark regression, we define extreme high-temperature days as the total number of days per month with a maximum temperature of 30°C or higher. This definition assumes that when temperatures exceed 30℃, industrial equipment consumes additional energy for cooling, leading to higher overall energy consumption, which could influence the decoupling effect. The results indeed show that an increase in extreme high-temperature days reduces the decoupling effect between economic growth and pollution emissions. However, due to the difficulty in defining a precise threshold for extreme high temperatures, we conduct a robustness check by redefining extreme high-temperature days as those between 28°C and 35°C. The results are presented in Table 7.
The results of this robustness test, shown in Table 7, reveal that even with this modified definition, HQDI continues to significantly increase the decoupling effect. Additionally, the negative impacts of extreme high-temperature days and the Herfindahl–Hirschman Index on decoupling remain statistically significant, consistent with the benchmark results. This sensitivity analysis indicates that the conclusions drawn in this study are robust and not sensitive to the specific choice of extreme high-temperature threshold. Regardless of how extreme high-temperature days are defined, HQDI has a robust and positive effect on decoupling, while extreme temperatures remain a significant factor hindering the decoupling of economic growth and pollution emissions.
This sensitivity analysis indicates that the conclusions drawn in this study are robust and not sensitive to the specific choice of the extreme high-temperature threshold. Therefore, we believe the findings of the study will hold even if alternative definitions of extreme high temperatures are used. Thanks again for your valuable feedback, which helps us improve the clarity and rigor of our analysis.
Comments 11: (Results and Discussion): The results are presented clearly, but more emphasis is needed on interpreting the practical significance of the findings. For example: How significant is a 26% increase in the probability of decoupling for policymakers or industry stakeholders? What are the practical or policy implications of this?
Response 11: Thank you for your insightful comment. We appreciate your suggestion to further emphasize the practical significance of the findings. In response, we have elaborated on the interpretation of the results in Table 4 to highlight the broader implications for policymakers and industry stakeholders. (Lines 532-550.)
Table 4 presents the impact of HQDI on the decoupling of economic growth and PM2.5 emissions. A stepwise regression approach is employed, beginning with HQDI alone in column (1) and progressively adding control variables such as extreme high-temperature days and the Herfindahl–Hirschman Index (HHI) in columns (2) and (3). All models control for industry and year fixed effects. The results consistently demonstrate a significant positive effect of HQDI on the decoupling effect, even after the inclusion of controls. This supports Hypothesis 1 of this study that high-quality development contributes to achieving the decoupling effects.
In column (3), the benchmark results show that a 1% increase in HQDI increases the likelihood of achieving decoupling between economic growth and PM2.5 by 26%, highlighting both statistical and economic significance. Specifically, compared with the average likelihood of decoupling of 0.52, this 26% increase equates to 50% higher odds of achieving decoupling, signaling a considerable shift toward more sustainable economic growth. For policymakers, these findings suggest that implementing strategies to enhance HQDI, such as investing in innovation and green industries, can significantly accelerate decoupling, resulting in more sustainable economic development. Industry stakeholders can note that aligning their business models with environmental sustainability goals contributes to reducing pollution and positions them to better meet future regulatory requirements and maintain long-term competitiveness.
This revised explanation in Section 4.3 further elaborates on the practical implications of these findings, reinforcing their relevance for both policy and industry decision-making. Thanks again for your constructive feedback, which greatly helps us clarify the practical significance of this study.
Comments 12: (Results and Discussion): Figures and tables could be better integrated into the text. For example, Figure 1 (distribution of decoupling statuses) is discussed only briefly, but it provides important context for understanding regional disparities.
Response 12: Thank you for your insightful comment. We apologize for the typo in our initial manuscript, as Figure 2 (not Figure 1) illustrates the distribution of decoupling statuses. And the updated order of figures is Figure 4 due to some adjustment according to expertise reviewers. We appreciate your suggestion to integrate the figures and tables more effectively into the text. In response, we detail the analysis of Figure 4 from both the overall distribution of decoupling statuses and the decoupling states across different cities. This expanded discussion is included in three paragraphs in Section 4.1. (Lines 507-530.)
4.1 Decoupling state of nine cities
Figure 4 illustrates the distribution of the decoupling states across nine prefecture-level cities in Fujian Province from January 2020 to June 2024, highlighting significant regional disparities. Despite these differences, most cities exhibit strong de-coupling (SD), with 174 out of 486 observations (35.80%), followed by weak decoupling (WD) with 80 observations (16.46%). These results suggest that most regions in Fujian have made notable progress in decoupling economic growth from pollution emissions.
Among the cities, Ningde, Zhangzhou, and Longyan show the highest proportion of SD (24 observations each), reflecting a successful balance between economic growth and PM2.5 control. In contrast, Xiamen and Quanzhou report lower SD proportions (15 and 13, respectively), likely due to a higher dependence on pollution-intensive eco-nomic activities. For WD, Fuzhou and Zhangzhou lead with 13 and 11 observations, respectively, while Xiamen has the highest proportion (13) of extended connectivity (END), reflecting varied environment–economy dynamics across development stages.
Other decoupling states also display regional variation. Fuzhou and Quanzhou show a higher prevalence of weak negative decoupling (WND), suggesting more pronounced pollution increases alongside economic growth. Meanwhile, Sanming, Longyan, and Nanping exhibit higher rates of relative decoupling (RD) and strong negative decoupling (SND), indicating continued reliance on pollution-intensive economic activities. Overall, these findings highlight the diverse challenges and complexities faced by Fujian’s cities in achieving sustainable decoupling of economic growth from environmental degradation.
Thank you again for your valuable feedback, which helps us improve the integration of the results into the discussion. We believe that the above description now provides a detailed analysis of the overall decoupling status in Fujian Province, as well as the varying decoupling states across different cities.
Comments 13: (Results and Discussion): The robustness checks are a strength of the paper but could be summarized more effectively. Currently, they appear scattered across the section.
Response 13: Thank you very much for your thoughtful comments. We appreciate your suggestion to summarize the robustness checks more effectively. In response, we consolidate the results of the robustness tests in Table 6 of the revised manuscript, improving clarity and readability. The revised robustness test results are discussed in the third and fourth paragraphs of Section 4.3 and Section 4.4. The key findings are summarized below. (Lines 572-662.)
4.3 Instrumental variable analysis of the impact of HQDI on the decoupling of economic growth from air pollution
As we found, HQDI can increase the likelihood that cities will achieve a decoupling state. However, it is necessary to discuss the validity of the adopted econometric model. The econometric model presented in this paper (Equation 8) may be subject to two potential issues. First, due to difficulties obtaining specific monthly economic indicators for prefecture-level cities, such as energy consumption structure, industrial structure, and policy variables, both the HQDI and decoupling effects could be influenced simultaneously. As a result, endogeneity problems may arise from the omitted variables. To address this, we control for both year and city fixed effects, which helps mitigate the impact of omitted variable bias on our estimation results.
Second, while HQDI influences the decoupling effect, the decoupling effect itself captures an optimal state where economic growth occurs alongside reduced pollution emissions. This relationship may, in turn, affect HQDI, potentially leading to reverse causality and endogeneity concerns. To mitigate these concerns, we adopt instrumental variables, specifically the lagged term of the HQDI and the average HQDI of cities other than the focal city, as commonly practiced in literature. The rationale for utilizing two instrumental variables is that when there is only one endogenous variable, the exogeneity of the instrumental variables can be tested using Hansen J statistic. The results of the instrumental variable regression are presented in Table 5.
The results in Table 5 suggest that the two instrumental variables significantly affect HQDI in the first stage at the 1% confidence level. Moreover, the Kleibergen–Paap rk Wald F statistic is 102.229, well above 10, indicating no issue of a weak instrumental variable. In the second stage, the P-value of the Hansen J statistic is 0.388, indicating that we cannot reject the null hypothesis of exogeneity for all instrumental variables, confirming their relevance and exogeneity. Regarding the regression coefficients, the impact of HQDI on decoupling from PM2.5 remains significantly positive at the 1% level, confirming the robustness of our results. The HQDI coefficient of 0.479 is 1.86 times higher than the benchmark regression value of 0.258, suggesting that potential endogeneity may lead to an underestimation of HQDI’s effect on decoupling. This underestimation is within an acceptable range, further supporting the robustness of our findings.
4.4 Robustness test of the impact of HQDI on the decoupling of economic growth from air pollution
4.4.1 Robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification
To verify the robustness of the benchmark results, this study conducts several robustness tests, including replacing the explained variable, involving outlier removal, adjusting the econometric model, and modifying the control variables. First, we redefine the decoupling from SO2 and decoupling from PM10 to reveal whether the impact of HQDI on the decoupling effect from different pollutants is still held. The results are shown in columns (1) and (2) of Table 6. Second, to ensure the prediction method does not bias the results, Zhangzhou is excluded from the analysis due to significant gaps in its pollution emission data, which are estimated using data from other cities and local temperature information in the baseline analysis; the results are reported in column (3) of Table 6. Third, given that the explanatory variables are binary indicators of strong or weak decoupling between economic growth and pollution emissions, a probit model is adopted to assess whether the findings are sensitive to the choice of regression model. The results of this analysis are presented in column (4) of Table 6. Then, the measurement of industry concentration is replaced with the market share percentage of the top four industries to provide a more precise representation of regional industry concentration, as shown in column (5) of Table 6. Finally, we add nonlinear research into HQDI on the decoupling effect, as shown in column (6) of Table 6.
The results in the first two columns in Table 6 suggest that HQDI positively impacts the decoupling from SO2 and PM10 at the 1% confidence level, confirming the robustness of HQDI on the decoupling effect from different pollutants. Moreover, the magnitude of coefficients reveals that a 1% increase in HQDI can increase the probabilities of decoupling from SO2 and PM10 by 44% and 42%, respectively. These findings underscore the role of HQDI in promoting sustainable development driven by innovation, green development, and technological progress. Fujian Province has actively embraced high-quality development principles, adjusting its industrial and energy structures to support decoupling efforts. Moreover, the results in columns (3)–(5) in Table 6 indicate that HQDI has a significant positive impact on the decoupling effect of PM2.5 at the 1% confidence level, thereby validating the reliability of the benchmark findings.
Table 6. Results of robustness test: Explained variable replacement, outlier removal, model adjustment, and control variable modification.
|
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
Replace explained variable |
Delete Zhangzhou |
Probit model |
Change control variable |
Nonlinear analysis |
||
|
Decoupling from SO2 |
Decoupling from PM10 |
Decoupling from PM2.5 |
||||
|
High-quality development index |
0.437*** |
0.415*** |
0.249*** |
0.958*** |
0.253*** |
-3.231*** |
|
(0.096) |
(0.097) |
(0.092) |
(0.362) |
(0.090) |
(1.070) |
|
|
Extreme high temperature |
-0.008*** |
-0.008*** |
-0.010*** |
-0.029*** |
-0.008*** |
-0.010*** |
|
|
(0.003) |
(0.003) |
(0.003) |
(0.008) |
(0.002) |
(0.003) |
|
Herfindahl-Hirschman Index |
0.134 |
0.601*** |
0.543** |
1.578** |
|
0.427** |
|
|
(0.211) |
(0.202) |
(0.211) |
(0.688) |
|
(0.195) |
|
CR4 |
|
|
|
|
1.046** |
|
|
|
|
|
|
|
(0.448) |
|
|
Square of High-quality development index |
|
|
|
|
|
0.471*** |
|
|
|
|
|
|
(0.149) |
|
|
Year fixed effects |
YES |
YES |
YES |
YES |
YES |
YES |
|
City fixed effects |
YES |
YES |
YES |
YES |
YES |
YES |
|
Observations |
486 |
486 |
432 |
486 |
486 |
486 |
|
R2 |
0.285 |
0.319 |
0.361 |
- |
0.331 |
0.344 |
4.4.2 Robustness test: Sensitivity analysis of extreme high temperature
In the benchmark regression, we define extreme high-temperature days as the total number of days per month with a maximum temperature of 30°C or higher. This definition assumes that when temperatures exceed 30℃, industrial equipment consumes additional energy for cooling, leading to higher overall energy consumption, which could influence the decoupling effect. The results indeed show that an increase in extreme high-temperature days reduces the decoupling effect between economic growth and pollution emissions. However, due to the difficulty in defining a precise threshold for extreme high temperatures, we conduct a robustness check by redefining extreme high-temperature days as those between 28°C and 35°C. The results are presented in Table 7.
The results of this robustness test, shown in Table 7, reveal that even with this modified definition, HQDI continues to significantly increase the decoupling effect. Additionally, the negative impacts of extreme high-temperature days and the Herfindahl–Hirschman Index on decoupling remain statistically significant, consistent with the benchmark results. This sensitivity analysis indicates that the conclusions drawn in this study are robust and not sensitive to the specific choice of extreme high-temperature threshold. Regardless of how extreme high-temperature days are defined, HQDI has a robust and positive effect on decoupling, while extreme temperatures remain a significant factor hindering the decoupling of economic growth and pollution emissions.
Table 7. Results of robustness test: Sensitivity analysis of extreme high temperature.
|
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
(7) |
(8) |
|
Decoupling from PM2.5 |
||||||||
|
High-quality development index |
0.211** |
0.233** |
0.258*** |
0.244*** |
0.235*** |
0.231*** |
0.208** |
0.177** |
|
(0.091) |
(0.090) |
(0.089) |
(0.087) |
(0.085) |
(0.084) |
(0.082) |
(0.080) |
|
|
Extreme high temperature (28℃) |
-0.006** |
|
|
|
|
|
|
|
|
(0.002) |
|
|
|
|
|
|
|
|
|
Herfindahl-Hirschman Index |
0.327* |
0.403** |
0.501** |
0.501** |
0.510** |
0.528** |
0.475** |
0.387* |
|
(0.193) |
(0.198) |
(0.202) |
(0.205) |
(0.206) |
(0.208) |
(0.204) |
(0.200) |
|
|
Extreme high temperature (29℃) |
|
-0.007*** |
|
|
|
|
|
|
|
|
(0.002) |
|
|
|
|
|
|
|
|
Extreme high temperature (30℃) |
|
|
-0.009*** |
|
|
|
|
|
|
|
|
(0.003) |
|
|
|
|
|
|
|
Extreme high temperature (31℃) |
|
|
|
-0.009*** |
|
|
|
|
|
|
|
|
(0.003) |
|
|
|
|
|
|
Extreme high temperature (32℃) |
|
|
|
|
-0.009*** |
|
|
|
|
|
|
|
|
(0.003) |
|
|
|
|
|
Extreme high temperature (33℃) |
|
|
|
|
|
-0.011*** |
|
|
|
|
|
|
|
|
(0.003) |
|
|
|
|
Extreme high temperature (34℃) |
|
|
|
|
|
|
-0.012*** |
|
|
|
|
|
|
|
|
(0.003) |
|
|
|
Extreme high temperature (35℃) |
|
|
|
|
|
|
|
-0.013*** |
|
|
|
|
|
|
|
|
(0.005) |
|
|
Year fixed effects |
YES |
YES |
YES |
YES |
YES |
YES |
YES |
YES |
|
City fixed effects |
YES |
YES |
YES |
YES |
YES |
YES |
YES |
YES |
|
Observations |
486 |
486 |
486 |
486 |
486 |
486 |
486 |
486 |
|
R2 |
0.324 |
0.327 |
0.333 |
0.331 |
0.330 |
0.331 |
0.329 |
0.326 |
Thanks again for your constructive feedback, which helps us present the robustness checks more clearly and strengthens the overall coherence of the results.
Comments 14: (Conclusion): The conclusion effectively summarizes key findings but misses an opportunity to provide actionable insights. For example:
What specific policies or programs should Fujian cities implement to further strengthen decoupling, especially in industries where brown industries dominate? Do you foresee this relationship breaking down at a certain point?
Response 14: Thank you for your insightful comments. We appreciate your suggestions for making the conclusion more actionable and focused on specific policy recommendations. In response, we expand the discussion to include targeted actions that Fujian cities can implement to further strengthen decoupling, especially in industries dominated by brown industries. These details are presented in the second paragraph of Section 5, as shown below. (Lines 739-753.)
Based on the study’s findings, several policy recommendations are proposed to promote sustainable economic growth while decoupling it from pollution emissions. Firstly, innovation and openness should be incentivized through R&D support and industry–university collaborations, fostering the adoption of cleaner technologies. Additionally, stronger policies to support green industries and environmentally friendly technologies are essential, as brown industries hinder decoupling. Energy efficiency standards for SMEs should be implemented, alongside financial and technical assistance, to reduce their energy consumption. To address the negative impacts of extreme weather, climate-resilient infrastructure and increased use of renewable energy should be prioritized. Furthermore, promoting market concentration can enhance energy efficiency and sustainability, though regulatory oversight is necessary to maintain competition. Expanding renewable energy usage and refining the HQDI to include emerging factors like digital technologies and circular economies will also support decoupling. These integrated policy measures aim to guide Fujian Province and similar regions toward a balanced and sustainable development trajectory.
Regarding your second question, we do not foresee a specific point at which the relationship between economic growth and pollution emission breaks, as our study primarily investigates how HQDI influences the probability of achieving strong or weak decoupling. Predicting such a breaking point is complex, and our analysis does not aim to forecast it. However, as shown in Figure 4, most observations (approximately 52.26%) fall into the categories of strong decoupling or weak decoupling, indicating that Fujian has made significant progress in decoupling economic growth from pollution emissions. With the continued promotion of high-quality development, we anticipate that Fujian will achieve complete decoupling between economic growth and pollution emissions in the foreseeable future.
Thanks again for your constructive feedback, which greatly contributes to improving the policy recommendations and overall impact of our conclusions.
Comments 15: (Conclusion): Could the HQDI framework be adopted by other provinces or countries? If so, what adaptations might be necessary? It would be useful to present a table with values (such as the extreme temperature threshold) which would probably need to be changed for different climates.
The study’s limitations are understated. For instance, acknowledging the potential inaccuracies in estimated electricity consumption coefficients or the limited geographic focus would make the discussion more balanced.
Suggest explicitly outlining directions for future research, such as the integration of renewable energy data or a broader cross-provincial analysis.
Response 15: Thank you for your valuable comments. We appreciate your suggestions regarding the limitations of the study and the potential adaptations required for applying the HQDI framework to other regions or countries. As response to comment 5 in introduction, the methodology proposed here is to be adopted in other provinces or countries only if the data for electricity consumption and price are available because the input-output table and pollution data are provided by several official database. We acknowledge the limitations of this study only focus on Fujian province for data limitations and provide suggestions for future research. Details are presented in the last paragraph in Section 5. The content is shown below. (Lines in 755-769.)
The decision to focus on Fujian Province in this study stems primarily from the availability of extensive electricity consumption data, which provides a solid foundation for our analysis. Additionally, Fujian’s industrial structure and significance within the broader national context make it a representative case for understanding high-quality growth strategies and decoupling processes. However, this focus on a single province is also a limitation of the study, as the findings may not be fully generalizable to regions with different industrial profiles or energy dynamics. Fortunately, the proposed methodology will be applied to other regions or countries once electricity consumption and price data are available. Future research could expand the geographical scope and incorporate additional variables, such as renewable energy usage, innovation capabilities, and policy interventions, to offer a more holistic understanding of decoupling dynamics across diverse regions. Moreover, investigating the impact of green technologies and government policies on decoupling could provide valuable in-sights into how sustainable economic growth can be further promoted, shedding light on the complex relationship between economic development and environmental sustainability.
Thanks again for your constructive feedback, which helps us improve the clarity and comprehensiveness of our study.
Comments 16: (Figures, Tables, and Visuals): Most tables are informative. However, Table 3 is quite dense to follow. I do not have an immediate suggestion for improvement, but maybe the authors can reflect on possible alternatives to present this data.
Response 16: Thank you for your valuable feedback on Table 3. We understand that the current format may appear dense, which could make it difficult for readers to interpret the information quickly. However, Table 3 is intended to present the descriptive statistics in a standardized format, and we believe that the information it contains is essential for understanding the data distribution. We are sorry that we have not thought of other ways to present descriptive statistics. If you have better suggestions, we are willing to correct them. Thank you once again for your constructive feedback.
Comments 17: (Figures, Tables, and Visuals): I would suggest adding the explanation of different abbreviations in the caption of the figures and tables where necessary. For example, the legend in Figure 2 contains abbreviations. It would be helpful to include the full terms in the caption of this figure for readability. Similar for Table 4, write DDEC in full.
Response 17: Thank you for your helpful suggestion. We update the captions of the figures and tables to include the full terms for all abbreviations where necessary. Specifically, we include the full terms in Figure 4, corresponding, delete all the abbreviations of decoupling state in Table 2 and in the appendix. The revised Table 2 (Lines 413-4114) and Figure 4 (Line 521-522) are as follows.
Table 2. Decoupling states of economic growth from air pollution.
|
Decoupling type |
Decoupling state |
DEC |
||
|
Decoupling |
Strong decoupling |
<0 |
>0 |
(-∞, 0) |
|
Weak decoupling |
≥0 |
≥0 |
[0, 0.8) |
|
|
Recession decoupling |
<0 |
<0 |
(1.2, +∞) |
|
|
Negative decoupling |
Weak negative decoupling |
≤0 |
≤0 |
[0, 0.8) |
|
Strong negative decoupling |
>0 |
<0 |
(-∞, 0) |
|
|
Expansive negative decoupling |
>0 |
>0 |
(1.2, +∞) |
|
|
Connection |
Recession connection |
<0 |
<0 |
[0.8, 1.2] |
|
Expansive connection |
>0 |
>0 |
[0.8, 1.2] |
Figure 4. Distribution of decoupling state of PM2.5 across cities.
We also write DDEC in full title of all Tables (Table 4, 8, A2, A3). These titles are shown below. The revised orders of Table A2 and A3 are due to the adjustment of robustness test.
Table 4. Regression results of HQDI on decoupling from PM2.5. (Line in 552.)
Table 8. Impact of HQDI sub-items on decoupling from PM2.5. (Line in 679.)
Table A2. Impact of all HDQI sub-items on decoupling from PM2.5 (1). (Line in 787.)
Table A3. Impact of all HDQI sub-items on decoupling from PM2.5 (2). (Line in 805.)
Since all DDEC are written in full, we delete the abbreviation of DDEC and redefine all the variables used in titles. The contents are shown below. (Lines in 418-422.)
To facilitate empirical analysis, we define a dummy variable of decoupling from one pollution; for example, decoupling from PM2.5 is set to 1 if the decoupling state of PM2.5 is strong or weak decoupling, and decoupling from PM2.5 is set to 0 for all other states. The same definition is applied to decoupling from SO2 and decoupling from PM10.
Thanks again for your constructive suggestions. These changes aim to improve the readability and clarity of the manuscript, ensuring that all terms are easily understood by the readers.
Comments 18: (Comments on the Quality of English Language): I would suggest thorough editing of this manuscript for the English language. Specific issues to watch out for:
The manuscript tends to use unnecessarily complex and technical phrases, especially in longer sentences. Simplifying language and breaking longer sentences into shorter, more direct sentences (without losing meaning) would make the text more accessible.
Be mindful for subject-verb agreement errors, especially in longer sentences.
Make sure terminology is used consistently throughout the manuscript.
Make sure to use straightforward language in both the abstract and conclusion to highlight both the novelty of the work, as well as the practical implications (for industry as well as policy makers).
Response 18: Thank you for your valuable feedback. We appreciate your suggestions regarding the quality of the English language in the manuscript. In response to your comments, we carefully edit the manuscript to simplify overly complex phrases and break longer sentences into shorter, more concise ones. We also address issues of subject-verb agreement, especially in the longer sentences, ensuring clarity and consistency throughout the text. Furthermore, we review and ensure that the terminology is used consistently, enhancing the readability and coherence of the manuscript. Lastly, we revise both the abstract and conclusion to highlight the novelty of our work and its practical implications for both industry and policymakers, using more straightforward language. We believe these revisions significantly improve the accessibility and clarity of the manuscript.
Due to the extensive revisions, changes are no longer marked separately in the revised manuscript. We are committed to revising the manuscript until it meets the highest academic standards.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsAfter the revision, the manuscript has been greatly improved. However, if the current "Literature review" (part 2) is divided into "Literature review" and "Theoretical Basis and Research Hypotheses", the structure and logic of this article will be more reasonable.
in addition, according to the format of Sustainability, please revise the format of this article further.
Comments on the Quality of English LanguageAlthough the current English expression does not affect the understanding of the manuscript, the authors should check the language again.
