Comments 1: The authors are advised to structure the abstract in accordance with the Instructions for Authors by explicitly addressing four components: 1) Background: Place the question addressed in a broad context and highlight the purpose of the study; 2) Methods: Describe briefly the main methods or treatments applied; 3) Results: Summarize the article’s main findings; and 4) Conclusion: Indicate the main conclusions or interpretations.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have revised the abstract to make its structure clearer and more consistent with the Instructions for Authors. Specifically, we added a concise background sentence to clarify the significance of green total factor productivity under resource and environmental constraints, retained and clarified the methods used in the study, summarized the main findings more explicitly, and added a concluding sentence to indicate the policy implications of the results. This revision can be found in the revised manuscript, Abstract section.

Comments 2: The current literature review appears to be a mechanical review of studies without sufficient comparative analysis. The authors should synthesize existing research by identifying common themes, contradictory findings, and research gaps, particularly regarding the spatial dynamics of GTFP measurement and the driving mechanisms at the urban scale in the Yangtze River Economic Belt.

Response 2: Thank you for this valuable suggestion. We agree that the literature review should not only summarize previous studies, but also provide a more synthetic discussion of existing research. Therefore, while retaining the original references and citation order, we have supplemented the literature review by adding a comparative discussion of previous studies on GTFP measurement, spatial heterogeneity, and driving mechanisms. We further clarified the remaining research gaps, especially the insufficient integration of GTFP measurement, dynamic decomposition, spatial evolution, and driving mechanism identification at the prefecture-level city scale in the Yangtze River Economic Belt. This revision can be found in the Introduction section of the revised manuscript.

Comments 3: While the Yangtze River Economic Belt is defined as a water system-linked region, the manuscript lacks explicit discussion of how the river basin’s hydrological characteristics, such as resource allocation, ecological connectivity, and pollution diffusion, influence green total factor productivity. The authors should supplement this by articulating the specific linkages between the water system and GTFP dynamics.

Response 3: Thank you for this helpful suggestion. We agree that the river-basin characteristics of the Yangtze River Economic Belt should be more clearly discussed. Therefore, we have added a concise explanation in the Study area section to clarify how the Yangtze River and its tributaries connect upstream, midstream, and downstream cities through water flows, ecological corridors, and pollution transmission pathways. We further explained that these hydrological linkages may affect GTFP through resource allocation, ecological connectivity, pollution diffusion, environmental governance, and cross-regional coordination. This revision can be found in Section 2.1 of the revised manuscript.

Comments 4: The selection criteria for input–output indicators, such as capital, labor, energy, desirable outputs, and undesirable outputs, need to be clearly justified. The authors should explain the theoretical or empirical basis for choosing these indicators, including their relevance to urban green development and consistency with existing literature.

Response 4: Thank you for this constructive comment. We agree that the rationale for selecting the input–output indicators should be stated more clearly. Therefore, we have supplemented the explanation in Section 2.2.2 to clarify that the selected indicators are based on the production process of urban green development and the common practice of GTFP measurement. Specifically, capital, labor, energy, land, and water are used to represent the main factor inputs; GDP and urban green space area are used to reflect desirable economic and ecological outputs; and industrial wastewater, industrial exhaust gas, and industrial smoke and dust emissions are used to represent the main environmental costs of industrial production. This revision strengthens the theoretical and empirical justification for the indicator system.

Comments 5: The manuscript should further justify the choice of the Super-SBM model and the Malmquist-Luenberger index. This includes comparisons with alternative methods such as DEA and SFA, and an explanation of why this approach is more suitable for capturing green productivity under resource and environmental constraints.

Response 5: Thank you for this helpful comment. We agree that the methodological rationale should be further clarified. Therefore, we have supplemented Section 2.3 by explaining why the Super-SBM model and the Malmquist-Luenberger index are appropriate for this study. Specifically, compared with traditional DEA models, the Super-SBM model can better account for input redundancy, desirable output shortfalls, and undesirable output excesses, and its super-efficiency form can further rank efficient cities. Compared with SFA, it does not require a predefined production function and is more suitable for multi-input and multi-output urban systems. In addition, the ML index can incorporate undesirable outputs into intertemporal productivity analysis and decompose GTFP changes into efficiency change and technological change. This revision can be found in Section 2.3 of the revised manuscript.

Comments 6: When classifying GTFP using the Jenks natural breaks method, the authors should clearly state the criteria for determining class boundaries, such as minimizing intra-group variance, and explain why this method is suitable for analyzing spatial heterogeneity.

Response 6: Thank you for this helpful suggestion. We have supplemented the explanation of the Jenks natural breaks method in Section 2.4.1. Specifically, we clarified that this method determines class boundaries by identifying natural groupings in the data, reducing variance within classes, and increasing differences between classes. We also explained that this method is suitable for revealing spatial heterogeneity in urban GTFP and for comparing the spatial distribution characteristics across different years.

Comments 7: The results on GTFP temporal dynamics show small numerical differences across periods. The authors should provide more basis for interpreting these trends, such as statistical significance, practical policy meaning, or comparison with other regions.

Response 7: Thank you for this helpful comment. We agree that the interpretation of the temporal dynamics of GTFP should be further clarified. Therefore, we have supplemented Section 3.1 by explaining the practical significance of relatively small changes in GTFP. Since GTFP is a composite index incorporating multiple inputs, desirable outputs, and undesirable outputs, even moderate changes can reflect improvements in green production performance under resource and environmental constraints. In addition, we emphasized that the increasing number and proportion of cities with GTFP values greater than or equal to 1 indicate a broader diffusion of green productivity improvement across the Yangtze River Economic Belt.

Comments 8: The trend surface plots for different years look visually similar, raising concerns about coding accuracy. The authors should carefully verify the spatial interpolation parameters and ensure that there is sufficient visual differentiation across years to reflect temporal changes in GTFP gradients.

Response 8: Thank you for this careful comment. We have rechecked the original data, spatial coordinates, trend surface fitting process, and mapping parameters for different years. After verification, we confirm that the data processing and mapping results are accurate. The visual similarity among the trend surface plots mainly reflects the relative stability of the macro-scale spatial gradient of GTFP in the Yangtze River Economic Belt during the study period, rather than an error in coding or mapping. Therefore, no further revision was made to this part.

Comments 9: The manuscript should emphasize the contribution of the integrated analytical framework combining Super-SBM ML index and geographical detector. Additionally, discuss the potential applicability of this methodology to other urban agglomerations or river basins, highlighting its generalizability and limitations.

Response 9: Thank you for this constructive suggestion. We have added a brief statement in the Discussion section to emphasize the contribution of the integrated analytical framework and its potential applicability to other urban agglomerations or river-basin regions.

Comments 1: However, there are some crucial aspects: the model of undesirable outputs only includes traditional industrial pollutants, completely ignoring carbon dioxide emissions. Assessing “green productivity” today without including decarbonization is a significant structural limitation.

Response 1: Thank you for this insightful comment. We fully agree that carbon dioxide emissions are important for evaluating green productivity, especially in the context of low-carbon transition and carbon neutrality. In this study, the undesirable outputs were selected mainly from the perspective of traditional industrial pollution, including industrial wastewater, industrial exhaust gas, and industrial smoke and dust emissions. This choice was made to ensure data availability, consistency, and comparability across 110 prefecture-level cities over the long period from 2007 to 2023. We acknowledge that incorporating CO₂ emissions would further enrich the measurement of GTFP. However, adding this indicator would require reconstructing the input–output system and recalculating the GTFP values, dynamic decomposition, spatial patterns, and driving-mechanism results. Therefore, in the present study, we retain the original indicator system and interpret the results mainly within the framework of traditional pollution and resource constraints. We will consider incorporating carbon-emission indicators in future research to provide a more comprehensive low-carbon assessment of urban green productivity.

Comments 2: In addition, the only ecology-related desirable output is “residential urban green space area”, a two-dimensional quantity that gives no information about actual ecological quality, ecosystem services, or performance in terms of functionality, effectiveness, utility, maintenance, evolution, or ecological network continuity.

Response 2: Thank you for this helpful comment. We agree that urban green space area cannot fully capture ecological quality, ecosystem services, or ecological network continuity. In this study, this indicator is used mainly to reflect the scale of urban ecological space supply, considering data availability and comparability across cities and years. To avoid overinterpreting this variable, we have added a brief clarification in Section 2.2.2, stating that urban green space area mainly reflects the quantity of ecological space rather than the full quality or functionality of urban ecosystems.

Comments 3: The conclusions should be better supported by the research results.

Response 3: Thank you for this helpful suggestion. We have revised the Discussion section to strengthen the connection between the empirical findings and the conclusions. Specifically, we added a concise statement emphasizing that the integrated results from the Super-SBM model, ML index, spatial analysis, and geographical detector model jointly support the conclusions on the measurement, spatial evolution, and driving mechanisms of urban GTFP. This revision helps make the conclusions more closely aligned with the research results.

Comments 1: Super-SBM Formulation: The stated constraints for inputs and outputs appear atypical for a super-efficiency SBM model handling undesirable outputs. Please clarify the orientation and the returns-to-scale assumptions. Provide the exact mathematical program implemented and address any potential infeasibility issues or frontier construction details.

Response 1: Thank you for this helpful comment. We have clarified the specification of the Super-SBM model in Section 2.3.1. Specifically, we added that the model used in this study is a non-radial and non-oriented Super-SBM model with undesirable outputs. In the model setting, inputs and undesirable outputs are expected to be reduced, while desirable outputs are expected to be expanded. We also clarified that the model was implemented using MAXDEA with the undesirable-output setting. This revision aims to make the model specification clearer without changing the original empirical framework.

Comments 2: ML Index Specifics: Specify whether you utilized a contemporaneous or global frontier, and clearly state the directional vector used.

Response 2: Thank you for this helpful comment. We have clarified the specific setting of the ML index in Section 2.3.2. In this study, the ML index is calculated based on the contemporaneous production frontier of adjacent periods. The direction vector is set to expand desirable outputs and reduce undesirable outputs, which is consistent with the objective of measuring green productivity under resource and environmental constraints.

Comments 3: Actionable Request: To mitigate contemporaneous frontier re-basing bias and ensure accurate intertemporal comparability, please implement a Global ML (GML) index as a robustness check.

Response 3: Thank you for this constructive suggestion. We agree that the Global Malmquist–Luenberger index is useful for improving intertemporal comparability under a global production frontier. In the present study, the ML index is used to decompose GTFP changes between adjacent periods and to identify the relative contributions of efficiency change and technological change. Considering the consistency of the current empirical framework and the focus of this study, we retained the ML-based dynamic decomposition. Meanwhile, we have added a brief clarification in Section 2.3.2, noting that future research may further apply the GML index to test cross-period comparability and robustness.

Comments 4: Capital and Energy Inputs: Proxying capital input with fixed-asset investment flows risks bias. Please construct a capital stock measure using the perpetual inventory method. Furthermore, total city electricity consumption conflates residential and industrial use. Replace this with total final energy use in standard coal equivalent (tce) or strictly industrial energy consumption.

Response 4: Thank you for this constructive suggestion. We agree that capital stock and more detailed energy-consumption indicators would be ideal for measuring capital and energy inputs. However, this study covers 110 prefecture-level cities in the Yangtze River Economic Belt over a long period from 2007 to 2023. For this spatial and temporal scale, fixed-asset investment and total electricity consumption are more consistently available and comparable across cities and years. Therefore, we use them as proxy variables to reflect the overall scale of capital input and energy-use pressure in urban production activities. Replacing these indicators would require reconstructing the input–output system and recalculating the GTFP values, decomposition results, spatial patterns, and driving-mechanism analysis. Considering the consistency of the current empirical framework, we retained the original indicators in this study.

Comments 5: Desirable Output: The inclusion of “urban green space area” as a desirable output risks conflating environmental amenity with market production technology. Please justify this indicator based on standard literature practice and provide a robustness check excluding this variable.

Response 5: Thank you for this helpful comment. We agree that urban green space area cannot fully represent the quality, functionality, or performance of urban ecosystems. In this study, this indicator is not intended to measure market production technology directly, but is used as a proxy for the ecological benefits and ecological space supply associated with urban green development. To avoid overinterpreting this variable, we have clarified its meaning in Section 2.2.2, noting that it mainly reflects the scale of urban ecological space supply rather than the full quality, functionality, or connectivity of urban ecosystems.

Comments 6: Undesirable Outputs: To improve environmental representativeness, please include standard air pollutants such as SO₂, NOx, PM2.5, and CO₂, or estimated carbon emissions based on energy data.

Response 6: Thank you for this valuable suggestion. We agree that including SO₂, NOx, PM2.5, and CO₂ would further improve the environmental representativeness of the undesirable outputs. In this study, industrial wastewater, industrial exhaust gas, and industrial smoke and dust emissions were selected mainly because they are consistently available and comparable across 110 prefecture-level cities during the long study period from 2007 to 2023. Adding additional pollutants or carbon-emission indicators would require reconstructing the input–output system and recalculating the GTFP values, decomposition results, spatial patterns, and driving-mechanism analysis. Therefore, we retained the original undesirable-output indicators in the present study, while acknowledging that future research can further incorporate carbon emissions and more detailed air pollutants to provide a more comprehensive assessment of urban green productivity.

Comments 7: Circularity in Drivers: Using energy intensity (EI) as a driver in the geographical detector while energy/electricity is already an input in the DEA stage may induce circular association. Please exclude overlapping components from the driver stage or provide sensitivity analysis showing that the results are not mechanically driven.

Response 7: Thank you for this important comment. We agree that potential overlap between the DEA input indicators and the driving factors should be carefully considered. In this study, electricity consumption in the DEA model is used to represent the scale of energy input in urban production activities, whereas energy intensity in the geographical detector reflects energy consumption per unit of economic output and is used to capture differences in energy-use efficiency across cities. Therefore, the two indicators have different analytical meanings. The former is an input variable for GTFP measurement, while the latter is an explanatory factor for spatial differentiation. Considering its theoretical relevance to green productivity and resource-use efficiency, we retained energy intensity as a driving factor in the geographical detector analysis.

Comments 8: Data Imputation and Coverage: Explicitly justify the city selection criteria (which YREB cities were excluded and why?). Quantify the extent of missing data (share of imputed values by variable/year), state the interpolation methods used, and confirm whether results are sensitive to these imputations.

Response 8: Thank you for this helpful comment. We have supplemented the description of data coverage and missing-value treatment in Section 2.2.1. Considering the missing data in some cities within the YREB, this study finally constructs a panel dataset covering 110 prefecture-level cities to ensure data availability and comparability. For a small number of missing values, interpolation, K-nearest neighbor imputation, and substitution methods were used according to data continuity and variable characteristics. The dataset was checked after imputation to ensure consistency across cities and years.

Comments 9: Intertemporal Comparability: Address how the analysis handles statistical and policy breaks, specifically the 2018 shift from pollution levies to the environmental protection tax, to ensure the environmental regulation indicators remain comparable over time.

Response 9: Thank you for this helpful comment. We agree that the institutional change from pollutant discharge fees to environmental protection tax after 2018 should be clarified. In this study, the environmental regulation indicator is constructed under the same policy logic of pollution-cost internalization. For the period after the policy adjustment, the corresponding fiscal revenue item related to environmental protection tax is used to maintain the continuity and comparability of the indicator. We have added a brief clarification in the variable description section.

Comments 10: Spatial Dependence: Since GTFP may exhibit spatial autocorrelation and spillover effects among neighboring cities, please test for spatial dependence or use spatial econometric models to avoid biased conclusions.

Response 10: Thank you for this constructive suggestion. We fully agree that GTFP may exhibit spatial autocorrelation and spillover effects among neighboring cities, especially in a highly connected river-basin region such as the Yangtze River Economic Belt. However, the main purpose of this study is to describe the spatiotemporal evolution of urban GTFP, decompose its dynamic changes, and identify the explanatory power of different driving factors. Therefore, the spatial part of this study focuses on spatial pattern analysis, trend surface analysis, center-of-gravity migration, and geographical detector analysis, rather than on estimating spatial spillover effects through spatial econometric models.

Introducing a spatial econometric model would require a different identification framework, including the construction and comparison of spatial weight matrices, spatial dependence diagnostics, model selection among SAR, SEM, SDM, and related specifications, and reinterpretation of the estimated coefficients as direct, indirect, and total effects. This would substantially expand the scope of the paper and may shift the focus away from the current integrated framework of GTFP measurement, dynamic decomposition, spatial evolution, and driving-factor detection. For this reason, we retained the current analytical framework, while acknowledging that spatial spillover effects are an important issue for future research.

Comments 11: Geographical Detector Discretization: Please clarify how continuous variables were discretized before applying the geographical detector model, including the discretization method and whether consistent parameters were used across years.

Response 11: Thank you for this helpful comment. We agree that the discretization procedure should be clarified because the geographical detector model requires categorical explanatory variables. Therefore, we have added a brief explanation in Section 2.5.1. Before applying the geographical detector model, continuous driving factors were discretized into categorical variables using the natural breaks method. The same discretization rule was applied across variables and years to maintain comparability in the factor detection results.

Comments 12: Center-of-Gravity Analysis: Please further explain the mechanism behind the center-of-gravity migration and provide robustness checks to ensure that the migration trajectory is not driven by outliers or minor numerical fluctuations.

Response 12: Thank you for this helpful comment. We agree that the interpretation of the center-of-gravity migration should be clarified. In this study, the center-of-gravity analysis is mainly used to identify the overall directional tendency of the spatial distribution of GTFP, rather than to estimate a causal mechanism. Since GTFP is an efficiency index with relatively moderate numerical variation, the observed migration trajectory is interpreted as a gradual adjustment in the spatial contribution structure of GTFP among cities in the Yangtze River Economic Belt. It does not imply a large-scale relocation of the regional productivity center. We have added a brief clarification in Section 3.2.3 to avoid overinterpreting the magnitude of the migration.

Comments 1: The manuscript contains a few typographical errors and awkward phrasings. For example, in Section 2.1, "the YREB connects the economically developed coastal areas with the vast inland hinterland and has long played a key role" could be streamlined for readability. A thorough language polishing by a native English speaker or a professional editing service is recommended.

Response 1: Thank you for this helpful suggestion. We have carefully polished the manuscript and corrected typographical errors and awkward expressions throughout the text. In particular, the sentence in Section 2.1 has been revised as follows:

“As a strategic corridor linking China’s developed coastal areas and inland hinterland, the YREB plays an important role in economic growth, industrial restructuring, and ecological security.”

Comments 2: In the References section, several entries have inconsistent formatting. For instance, reference [9] (He et al., 2021) and reference [14] (Song et al., 2018) use slightly different punctuation styles. The authors should ensure all references strictly follow the journal's style guidelines.

Response 2: Thank you for pointing this out. We have carefully checked and revised the References section. The formatting of all references has been standardized according to the journal’s style guidelines, including author names, punctuation, journal titles, volume numbers, page ranges, article numbers, and DOI formats. The formatting inconsistencies in references [9] and [14], as well as other similar issues, have now been corrected.

Comments 3: In Table 2, the unit for GDP per capita (PGDP) is listed as "CNY/person" but the description reads "Regional economic development affects green technology diffusion..." — the description text is somewhat lengthy for a table; consider shortening it.

Response 3: Thank you for this valuable suggestion. We have shortened the description of GDP per capita (PGDP) in Table 2 to make it more concise and suitable for tabular presentation. The revised description is:

“Economic development influences green technology diffusion, industrial upgrading, and environmental governance.”

This revision retains the original meaning while improving the conciseness of the table.

Comments 4: Figure 2 and Figure 3 are described in the text but the resolution of the figures in the current PDF appears suboptimal for print. The authors should ensure all figures meet the journal's minimum resolution requirements (typically 300 dpi).

Response 4: Thank you for this important reminder. We have replaced Figure 2 and Figure 3 with higher-resolution versions that meet the journal’s figure quality requirements. The revised figures are clearer and more suitable for publication and print presentation.

Once again, we sincerely thank the reviewer for the careful evaluation and constructive suggestions. These comments have helped us further improve the clarity, formatting consistency, and presentation quality of the manuscript.