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Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region

Land 2025, 14(1), 95; https://doi.org/10.3390/land14010095

by Kangjuan Lv¹

, Qiming Wang^2,*, Xunpeng Shi³

, Li Huang¹ and Yatian Liu⁴

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Reviewer 4: Anonymous

Reviewer 5:

Ihor Petrushka

Land 2025, 14(1), 95; https://doi.org/10.3390/land14010095

Submission received: 2 December 2024 / Revised: 26 December 2024 / Accepted: 3 January 2025 / Published: 6 January 2025

(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript (land-3376609) tries to present the matching of land use type data with nighttime light data (Land use-NTL) to estimate carbon dioxide generated from fossil fuel combustion (CEF). Accurately monitoring the spatial patterns of carbon dioxide emissions can assist governments in scientifically formulating relevant policies. After reading the current manuscript, I have the following major serious concerns and comments:

- . The data used in the manuscript mainly come from the national or provincial level, and the data at the city and county levels lack accuracy, which limits the depth of the study.

- . Many studies have proposed optimized or corrected night light data products. Why does this study need to correct the original night light data itself? Has the effect of the correction been effectively tested?

- . The selection and verification process of the models (fixed effects and geographically weighted time regression) in this study may not be sufficient, and the applicability and accuracy of different models cannot be fully evaluated.

- . In addition, the models may not fully consider the complexity and diversity of carbon emissions, such as the differences in carbon emission characteristics in different industries and regions.

- . There is a lack of detail in describing the research methods. For example, in Section 3.4.2, the methods for quantifying and counting carbon dioxide emissions, the specific IPCC methods and parameter settings, the linear regression model construction process, variable selection, data preprocessing and other details are not given.

- . The literature review does not cover the cutting-edge research in related fields, especially the latest research results in this year. Please refer to:

Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information, 2024.

Estimation of carbon emissions from different industrial categories integrated nighttime light and POI data—A case study in the Yellow River Basin.

- . In the process of data integration, this study processed data sets of different time ranges, but there may be errors in the connection and matching between different data sets, which will affect the accuracy of the final results.

- . When summarizing the studies on estimating carbon emissions using nighttime light data, there is no comprehensive and systematic review of the advantages and disadvantages of these studies, as well as their relationship and differences with this study.

- . In the results analysis section, the manuscript has relatively few in-depth analysis and explanations of the total amount, growth rate, and spatial distribution of carbon emissions in the study area. For example, it only mentions the significant decline in carbon emissions in Shanghai, but does not explore the reasons and mechanisms behind this change. Furthermore, there is a lack of in-depth comparison and analysis of the differences and trends in carbon emissions between different regions.

- . In addition, this manuscript only describes the trends and changes in carbon emissions, and lacks analysis and discussion of the driving factors, influencing factors, and policy recommendations behind them.

- . In the Discussion Section, there is a lack of in-depth discussion on the policy implications and practical significance of the research results. For example, there is no discussion on how the research results can provide scientific basis and decision-making support for the government to formulate low-carbon policies and promote sustainable development of regional economies.

- . In addition, the Conclusion Section may also be able to more clearly point out the practical application value and policy implications of the research.

- . The aesthetics of the Figures in this manuscript need to be improved, and the font size also needs to be increased.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Dear Editor:

Thank you for your prompt feedback and for the reviewer’s comments concerning our manuscript entitled “Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region” (Manuscript ID: land-3376609). We would like to express our sincere gratitude to the reviewers for their constructive and positive comments, which helped us to improve the quality of the paper in depth. The original version of the paper has been improved. Revisions to the content are shown in the document "Revised Manuscript-Marked". In response to the editor and the reviewers’ comments, we addressed them point-by-point. Responses are in red. The item-by-item responses to the reviewer’s comments are listed below.

Responses to the reviewer‘s comments:

Reviewer #1:

Response: Thank you very much for taking the time to review this manuscript. Below are our detailed responses and the corresponding revisions highlighted in the resubmitted files.

Comments 1: The data used in the manuscript mainly come from the national or provincial level, and the data at the city and county levels lack accuracy, which limits the depth of the study.

Response1: Thank you very much for your valuable feedback. I'm glad you raised some comments on this issue. While China has authoritative national and provincial-level carbon emissions data that follows IPCC guidelines, there is no comprehensive, publicly available cities-level carbon emission data or county-level carbon emission data as of now. It is important to improve the granularity of emission data at smaller administrative levels, including counties. In response to this situation, we attempt to use remote sensing data (including NTL and Landuse) to estimate carbon emissions at finer spatial and sectoral scales. This approach is particularly useful in the absence of direct emissions reports, but it is important to note that these data should only be considered proxy data rather than precise measurement data.

To illustrate the importance of the refined data, we have improved the explanation of this aspect in the Introduction. The original text follows:

“Refined carbon emission data enhance our ability to study how microeconomic activities influence carbon emissions, allowing for a more nuanced analysis of the temporal dynamics of emissions in relation to regional economic activities and land use patterns. More detailed county carbon emission data will enhance policymakers' understanding of regional emission characteristics, enabling better evaluation of past policies' impact on various sectors. This data will help identify which governance strategies are most effective and differentiate carbon emission efficiency and reduction costs across industries. By considering emissions reduction costs alongside welfare effects, policymakers can better balance environmental goals with economic growth, allowing for tailored policies in different regions that achieve maximum emission reductions at minimal cost. The compilation and analysis of carbon emission data at the county level facilitate a more precise identification of local emission sources and enable the formulation of effective strategies that align with national emission reduction targets. For enterprises, precise carbon emission data enables the identification of long-term economic impacts from investments in innovation and low-carbon technologies. By leveraging existing measures, companies can mitigate potential future costs associated with environmental regulations. A long-term analysis of sector-specific carbon emissions will allow businesses to accurately determine these costs, highlighting the practical significance of this article.”

Comments 2: Many studies have proposed optimized or corrected night light data products. Why does this study need to correct the original night light data itself? Has the effect of the correction been effectively tested?

Response2: Thank you very much for your valuable feedback. Currently, commonly used nighttime light datasets include DMSP and VIIRS data. Both datasets require pre-calibration during practical use. DMSP suffers from systematic biases and light saturation issues due to the lack of onboard calibration and sensor degradation. VIIRS, on the other hand, is not filtered and contains numerous transient light sources, such as lasers, fires, ships, etc. The presence of these errors can lead to biased estimation coefficients, resulting in inaccurate carbon emission estimates. Therefore, it is also necessary to calibrate these datasets in this study.

This study utilizes established methods from existing literature to calibrate the DMSP and VIIRS nighttime light datasets, ensuring that our analysis relies on reasonable and effective data. The original text follows:

“Nighttime light data has been widely applied in various studies. To obtain more valuable long-term time series data, existing research often uses a fusion of DMSP-OLS and NPP-VIIRS datasets. DMSP-OLS suffers from sensor aging issues, resulting in systematic biases and light saturation. These problems are not present in NPP-VIIRS; however, NPP-VIIRS itself has issues with outliers, such as unfiltered transient light sources (e.g., lasers, ships, aircraft). These distortions affect the accuracy of carbon emission estimation, so it is necessary to calibrate both datasets before their fusion. Additionally, one of the datasets provides annual data, while the other provides monthly data, and their resolutions are inconsistent. Therefore, mutual calibration and fusion of the two datasets are also required to ensure data consistency and accuracy.

Following the common processing methods in existing literature, we collated and integrated the two sets of data. Firstly, NOAA's F162006 DMSP-OLS data were selected as reference data, using Hegang as the pseudo-invariant calibration region with a quadratic calibration model, following Elvidge's approach (1997). Secondly, NPP-VIIRS data noise removal is essential due to the lack of filtering in the nighttime light data. For anomalous values in the original images with DN values less than 0, they were reassigned as 0. To account for high DN values likely representing noise (e.g., from airports or fires), Beijing, Shanghai, and Guangzhou were used as reference points, with peak DN values in other regions theoretically not exceeding these cities. By optimizing the monthly data, we further obtain annual data and perform annual calibration based on the yearly data to ensure that there are no abnormal DN (Digital Number) values. Thirdly, all spatial data were referenced to the WGS-84 datum, and the spatial resolution was resampled to 1 km. Finally, logarithmic transformation of the NPP-VIIRS data reduced variance in radiance values, and by selecting 2013 as the common coverage year, a sigmoid model was applied to convert the NPP-VIIRS data into a curve based on DMSP-OLS and the logarithmically transformed NPP-VIIRS data.”

Comments 3: The selection and verification process of the models (fixed effects and geographically weighted time regression) in this study may not be sufficient, and the applicability and accuracy of different models cannot be fully evaluated.

Response3: Thank you for your thoughtful comments regarding the selection and validation of the models in our study. We reviewed the manuscript and noted that the model selection section needs a clearer explanation. We will revise it to include a detailed description of the model validation process, enhancing the clarity of our methodology and ensuring our conclusions are based on a thorough assessment of the model's performance.

And we acknowledge that a comprehensive validation is crucial for evaluating the robustness of our findings. Therefore, in Section 4.2, we compared the performance of the GWTR model with alternative models such as the panel fixed model and OLS regression. Based on performance metrics such as R-squared, MAE, and RMSE, the results in Table 3 show that the R-squared of the GWTR model is consistently the highest, while the MAE and RMSE are consistently the lowest compared to the other two models. The original text follows:

“In earlier related studies, most researchers adopted basic linear regression models such as OLS. While OLS has the advantage of being simple and straightforward, it is unable to account for individual-specific effects, leading to biases in carbon emissions modeling across individuals and time. Panel estimation models have been widely applied to carbon emission estimation; however, the estimation strategies of panel fixed-effects regression also have certain limitations. For instance, these models typically assume that coefficients remain constant over space and time, which fails to account for localized effects. To address this issue, we further introduce the GTWR model to overcome such limitations. First, GTWR allows the relationship between dependent and independent variables to vary across both space and time. This is particularly important in carbon emission estimation, as the complexity and diversity of carbon emissions result in differences across regions and over time. Second, GTWR permits local estimation of coefficients, which is especially useful in regional studies. Regional factors such as economic structures and policy environments vary across locations, and a single coefficient is insufficient to capture the nuanced spatiotemporal variations in carbon emissions. GTWR, by capturing local effects and spatiotemporal heterogeneity, typically achieves better model fit and higher explanatory power compared to panel fixed-effects models.“

“In addition, this study incorporates the Spatial Autoregressive Model (SAR) and Spatial Durbin Model (SDM) to examine whether these models can provide more accurate estimations. According to the results of the Akaike Information Criterion (AIC), the AIC value of the GTWR model is significantly smaller than those of the SAR and SDM models, indicating that the GTWR model is the more appropriate choice. Furthermore, the calculation of precision metrics also shows that the GTWR model achieves higher accuracy than both the SAR (RMSE = 0.6301; MAE = 0.2602) and SDM (RMSE = 0.6505; MAE = 0.2825) models. This further demonstrates the rationality of using the GTWR model for carbon emission estimation.”

Comments 4: In addition, the models may not fully consider the complexity and diversity of carbon emissions, such as the differences in carbon emission characteristics in different industries and regions.

Response4: Thank you very much for your valuable feedback. We appreciate the importance of considering these factors in building a more accurate and comprehensive model. We have carefully considered this feedback and would like to clarify our approach, as well as outline potential avenues for addressing these concerns. The original text follows:

“we focus on using nighttime light data as a proxy for carbon emissions, which serves as a macro indicator of carbon emissions at the regional level. Additionally, considering the heterogeneity across industries and regions, we incorporate land use data and the GTWR model for further identification. However, due to the lack of more granular carbon emission data, we were unable to further refine the analysis at the industry level. Previous studies have used input-output tables to achieve more detailed sectoral carbon emission estimates; however, due to the large time span of input-output tables, the fit is not ideal. In the future, with updated data, subsequent research can consider improving the carbon emission estimates for different sectors, which will provide more accurate cross-industry and cross-region carbon emission estimates. This will help capture the diversity and complexity of carbon emissions across regions and industries more accurately. Finally, the nighttime light data offers carbon emission grid data at a 1km*1km resolution, failing to capture detailed urban emission patterns. Future research should explore higher-resolution satellite data for more precise carbon emissions analysis.”

Comments 5: There is a lack of detail in describing the research methods. For example, in Section 3.4.2, the methods for quantifying and counting carbon dioxide emissions, the specific IPCC methods and parameter settings, the linear regression model construction process, variable selection, data preprocessing and other details are not given.

Response5: Thank you so much for your invaluable suggestion. I have incorporated more details into the manuscript and appendix. The original text follows:

“The specific regression model for GTWR is as follows:

Where, represents the value of the carbon emission for the k sector. represents the longitude and latitude coordinates. represents time. is the model error term. is the regression coefficient corresponding to the nighttime light data for the k sector at the i sample.”

Comments 6: The literature review does not cover the cutting-edge research in related fields, especially the latest research results in this year. Please refer to:

Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information, 2024.

Estimation of carbon emissions from different industrial categories integrated nighttime light and POI data—A case study in the Yellow River Basin.

Response6: Thank you so much for your invaluable suggestion. I completely agree that including the latest studies is essential for aligning the paper with current trends and advancements. We have updated the literature review to accurately reflect the progress in existing research. The original text follows:

“There are currently two main methods for more refined spatial carbon emission estimation. The first method is based on existing energy balance sheets. For instance, Chen (2021) estimated industrial carbon emissions in Guangdong Province by compiling energy consumption and cement production data from various prefecture-level cities, depicting the spatiotemporal pattern of industrial carbon emissions in Guangdong Province from 2005 to 2015[29]. Dong(2018) used input-output tables and IPCC methods to calculate carbon dioxide emissions in four direct-controlled municipalities, namely Beijing, Tianjin, Shanghai, and Chongqing, And the study identified the urbanization rate as the primary driver of urban carbon dioxide emissions increase[30]. These studies rely on the collection of field survey data, which are not updated frequently. For example, China's input-output tables are compiled every five years. The second issue is the inevitable presence of various noises and biases in survey data, affecting the research and decision-making based on these data[31]. The third issue is that the field survey method is too costly and lacks sustainability. The second method is to use remote sensing data for estimation. With the advancement of remote sensing technology, estimating economic activities through satellite data has emerged as a new method[32]. Among these, NTL, highly correlated with human activities, is often used by scholars as a proxy variable to investigate human activities[33]. Elvidge et al. (1997) laid the groundwork for understanding the correlation between NTL and carbon emissions. Their work demonstrated the potential of using NTL as a proxy variable to measure carbon emissions[34]. Chen (2020) estimated carbon emissions data for Chinese countries from 2000 to 2017 using NTL[10]. Wang (2023) combined multi-source remote sensing data to estimate carbon emissions at the grid scale in China from 2010 to 2018 and explored potential driving factors for carbon emissions using Hunan Province as an example[35]. For more detailed research, Zheng (2024) estimated carbon emissions patterns at the "province-city-county-township" four-level scale in Fujian Province using NTL[36]. Zhang (2024) studied carbon emissions at the street level in Xi'an using NTL[37]. Wu (2025) estimated energy-related carbon emissions in the Northeast by developing a model linking nighttime light (NTL) to emissions. He also applied the Tapio decoupling model to examine the relationship between economic development and carbon emissions, concluding that both follow a three-stage decoupling pattern, with an overall state of decoupling marked by a growth linkage[38]. Lu (2024) utilized high-resolution NTL data obtained from the domestic satellite Luojia 1–01 to estimate electricity consumption in Shenzhen[39].

To further enhance the accuracy of the estimation results, existing studies have made substantial efforts. Some studies have considered incorporating additional data to construct more refined carbon emission estimation models. Meng (2017) further improved the estimation accuracy (R2=0.8796) by introducing data such as population density and combining it with NTL to estimate carbon emissions[40]. Wang (2023) combined NTL and XCO2 concentration data to develop a carbon emission and energy consumption estimation model, achieving spatially refined measurements of energy consumption carbon emissions[41]. In terms of model selection, existing studies have identified a strong linear correlation between carbon emissions from human energy consumption, which is why linear regression analysis is often used with regional carbon emission statistics and nighttime light data[42]. Considering that CEF between cities is not isolated, with one region's emissions being influenced by surrounding cities, an SDM is employed to address the spatial dependence issue in NTL-based CEF estimation, and the use of a dynamic SDM model addresses endogeneity problems [43]. Considering the spatial heterogeneity, in addition to the application of the SDM model, some studies have incorporated the Geographically Weighted Regression (GWR) model into this estimation framework, aiming to improve the accuracy of the estimates[44].

Most related research primarily focuses on total carbon dioxide emissions, with little distinction between emissions from different sectors. Shi (2020) has started investigating the relationship between NTL and carbon emissions across various sectors. The study findings indicate that NTL can provide more accurate carbon emission assessments in urban areas with large populations and relatively developed social and economic conditions, and that the precision of estimating urban carbon emissions through NTL is higher than that of estimating industrial carbon emissions[45]. POI data, as a form of multi-source geographic spatial big data, can be combined with NTL to obtain carbon emission estimates for specific sectors[46]. Wei (2024) effectively measured industrial carbon emissions in the Yellow River Basin by combining NTL and land use data, and further classified industrial carbon emissions using POI data. They categorized industrial carbon emissions into eight sectors and analyzed them individually[47]. Apart from POI, Landuse data is also commonly used to represent regional carbon emissions. Liu (2024) studied county-level Landuse carbon emissions (LUCE) using changes in China’s land use data[48]. Since directly using NTL data results in carbon emission spatialization with high-value areas overly concentrated, making it difficult to discern the internal spatial heterogeneity, combining Landuse data helps to accurately depict carbon emissions[49]. Wei (2021) estimated carbon emissions for various provinces in China by differentiating NTL data under different land use types, further refining the categories into urban, rural, and industrial sectors[50].

In the field of using NTL to measure carbon emissions, existing studies have established a relatively comprehensive research framework. Although these studies have made significant progress, there are still certain limitations. First, current research mostly focuses on depicting the total carbon emissions, and NTL alone cannot differentiate between different categories or industries of carbon emissions. Secondly, aggregating NTL data to simulate carbon emissions masks the spatial heterogeneity of emissions, resulting in overly simplified carbon emission views based on average estimates, and failing to capture more effective and refined differences.

The connection and distinction between this study and existing research lie in the fact that this study draws on existing mature approaches, such as the processing of nighttime light data and the calculation of carbon emissions. At the same time, this study attempts to address the issue of sector-specific carbon emission characterization by incorporating Landuse data and solving spatial heterogeneity through the application of the GTWR model. Building on existing research, we believe it is essential to consider both sectoral and spatial heterogeneity when estimating CEF using NTL. Therefore, we propose the research hypotheses of this study. Integrating nighttime light data with land use data will yield more accurate carbon emission estimates by accounting for spatial and sectoral differences.”

The new references are as following:

Gao, F., Wu, J., Xiao, J., Li, X., Liao, S., & Chen, W. (2023). Spatially explicit carbon emissions by remote sensing and social sensing. Environmental Research, 221, 115257.

Liu, C., Hu, S., Wu, S., Song, J., & Li, H. (2024). County-level land use carbon emissions in China: spatiotemporal patterns and impact factors. Sustainable Cities and Society, 105304.

Lu, S., Xiao, Y., Lu, Y., & Lin, J. (2024). Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information. Geo-spatial Information Science, 1-14.

Meng, L., Graus, W., Worrell, E., & Huang, B. (2014). Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China. Energy, 71, 468-478.

Wang, G., Hu, Q., He, L., Guo, J., Huang, J., & Zhong, L. (2024). The estimation of building carbon emission using nighttime light images: A comparative study at various spatial scales. Sustainable Cities and Society, 101, 105066.

Wang, M., Wang, Y., Teng, F., & Ji, Y. (2023). The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data. Journal of Environmental Management, 346, 119054.

Wei, W., Chen, D., Zhang, X., Ma, L., Xie, B., Zhou, J., ... & Yan, P. (2024). Estimation of carbon emissions from different industrial categories integrated nighttime light and POI data—A case study in the Yellow River Basin. Journal of Environmental Management, 370, 122418.

Wu, H., Yang, Y., & Li, W. (2024). Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data. Sustainable Cities and Society, 113, 105712.

Wu, R., Wang, R., Nian, Z., & Gu, J. (2025). Spatio-temporal variation and decoupling effects of energy carbon footprint based on nighttime light data: Evidence from counties in northeast China. Atmospheric Pollution Research, 16(2), 102366.

Xia, B. (2024). Spatial Characteristics and Driving Mechanisms of Carbon Neutrality Progress in Tourism Attractions in the Qinghai–Tibet Plateau Based on Remote Sensing Methods. Remote Sensing, 16(23), 4481.

Zhang, W., Cui, Y., Wang, J., Wang, C., & Streets, D. G. (2020). How does urbanization affect CO2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data. Journal of Cleaner Production, 276, 123188.

Comments 7: In the process of data integration, this study processed data sets of different time ranges, but there may be errors in the connection and matching between different data sets, which will affect the accuracy of the final results.

Response7: Thank you so much for your invaluable suggestion.The discontinuity between DMSP and VIIRS data creates inconsistencies in growth estimation, complicating the development of reliable long-term carbon emission indicators from a single dataset. Thus, integrating the two datasets is essential. This study adheres to established practices in the extensive literature on nighttime light data integration, ensuring that the fused dataset used is both reasonable and effective. The original text follows:

“Following the common processing methods in existing literature, we collated and integrated the two sets of data. Firstly, NOAA's F162006 DMSP-OLS data were selected as reference data, using Hegang as the pseudo-invariant calibration region with a quadratic calibration model, following Elvidge's approach (1997). Secondly, NPP-VIIRS data noise removal is essential due to the lack of filtering in the nighttime light data. For anomalous values in the original images with DN values less than 0, they were reassigned as 0. To account for high DN values likely representing noise (e.g., from airports or fires), Beijing, Shanghai, and Guangzhou were used as reference points, with peak DN values in other regions theoretically not exceeding these cities. By optimizing the monthly data, we further obtain annual data and perform annual calibration based on the yearly data to ensure that there are no abnormal DN (Digital Number) values. Thirdly, all spatial data were referenced to the WGS-84 datum, and the spatial resolution was resampled to 1 km. Finally, logarithmic transformation of the NPP-VIIRS data reduced variance in radiance values, and by selecting 2013 as the common coverage year, a sigmoid model was applied to convert the NPP-VIIRS data into a curve based on DMSP-OLS and the logarithmically transformed NPP-VIIRS data.”

Comments 8: When summarizing the studies on estimating carbon emissions using nighttime light data, there is no comprehensive and systematic review of the advantages and disadvantages of these studies, as well as their relationship and differences with this study.

Response8: Thank you so much for your valuable feedback. In response, we added the strengths and weaknesses of current research in the literature review section, while further clarifying the connections and differences between our research and existing studies. The original text follows:

“In the field of using NTL to measure carbon emissions, existing studies have established a relatively comprehensive research framework. Although these studies have made significant progress, there are still certain limitations. First, current research mostly focuses on depicting the total carbon emissions, and NTL alone cannot differentiate between different categories or industries of carbon emissions. Secondly, aggregating NTL data to simulate carbon emissions masks the spatial heterogeneity of emissions, resulting in overly simplified carbon emission views based on average estimates, and failing to capture more effective and refined differences.“

“The connection and distinction between this study and existing research lie in the fact that this study draws on existing mature approaches, such as the processing of nighttime light data and the calculation of carbon emissions. At the same time, this study attempts to address the issue of sector-specific carbon emission characterization by incorporating Landuse data and solving spatial heterogeneity through the application of the GTWR model. Building on existing research, we believe it is essential to consider both sectoral and spatial heterogeneity when estimating CEF using NTL. Therefore, we propose the research hypotheses of this study. Integrating nighttime light data with land use data will yield more accurate carbon emission estimates by accounting for spatial and sectoral differences.”

Comments 9: In the results analysis section, the manuscript has relatively few in-depth analysis and explanations of the total amount, growth rate, and spatial distribution of carbon emissions in the study area. For example, it only mentions the significant decline in carbon emissions in Shanghai, but does not explore the reasons and mechanisms behind this change. Furthermore, there is a lack of in-depth comparison and analysis of the differences and trends in carbon emissions between different regions. In addition, this manuscript only describes the trends and changes in carbon emissions, and lacks analysis and discussion of the driving factors, influencing factors, and policy recommendations behind them.

Response9: Thank you for your valuable feedback on the need to analyze the driving factors and policy recommendations related to carbon emissions trends. We recognize the importance of understanding the underlying causes of these changes to enhance the study's broader implications. However, the primary objective of this paper is to measure carbon emissions at a high spatial resolution using NTL data as a proxy for economic activity. This methodological approach and the focus of the study are primarily concerned with quantifying emissions trends over time, rather than providing a comprehensive analysis of the drivers behind those trends. As such, the causal analysis of carbon emission drivers is not within the scope of this study. However, we do acknowledge that understanding the driving forces behind emission trends is crucial for formulating targeted emission reduction strategies. While the current paper does not delve into these drivers, we believe that the measured carbon emissions data provided here can serve as a foundation for future studies that explore these factors in greater detail.

In response to your feedback, we will acknowledge the importance of these aspects and suggest them as key areas for future research that can build upon the findings of the current study. We believe this will help expand the scope and relevance of this work in the context of carbon management and policy formulation. Thank you again for your thoughtful suggestion.

Comments 10: In the Discussion Section, there is a lack of in-depth discussion on the policy implications and practical significance of the research results. For example, there is no discussion on how the research results can provide scientific basis and decision-making support for the government to formulate low-carbon policies and promote sustainable development of regional economies.

Response10: Thank you for your valuable feedback. We will revise the discussion section to clearly highlight the policy implications of the study, especially how the spatial emissions data can assist decision-makers in developing low-carbon strategies. The original text follows:

“Observing the local characteristics of the region, we identified carbon emission hotspots in the Yangtze River Delta. In the early years, carbon emissions in the Yangtze River Delta were primarily concentrated in Shanghai, but over time, they gradually expanded to surrounding areas. Meanwhile, other cities in the region, such as Nanjing, Hefei, Yangzhou, and Ningbo, also emerged as secondary growth poles for carbon emissions. In 2000, Shanghai's CEF emissions accounted for 22.34%. By 2020, Shanghai's share had decreased to 11.97%. Jiangsu Province has the highest proportion, with its CEF emissions accounting for 34.39% of the total in 2020, rising to 43.02% by 2020. This is mainly due to Jiangsu Province's rapid industrial development and its absorption of outdated production capacity transferred from Shanghai. This indicates that over the past 20 years, carbon emissions in the Yangtze River Delta have shown spillover effects. In addition to the original growth pole, Shanghai, new secondary growth poles have emerged. Therefore, it is essential to focus on the carbon emissions from the CEF centers and implement targeted policy interventions. At the same time, due to the existence of spillover effects, achieving carbon reduction through independent local governance models has become increasingly difficult. Thus, coordinated carbon reduction efforts among urban agglomerations are urgently needed to promote high-quality development and effectively reduce regional CEF emissions. According to the second law of geography, which states that spatial separation creates differences among geographic features, there is significant heterogeneity in sectoral carbon emissions across regions. The distribution of carbon emissions also varies by sector. Industrial CEF emissions are primarily concentrated in Shanghai's Minhang District, Jiading District, and Pudong New Area, as well as in surrounding areas such as Suzhou, Hangzhou, and Ningbo. Transportation CEF emissions are more spatially dispersed, forming small high-density clusters around key transportation hubs such as Hefei, Nanjing, Yangzhou, and Wuxi. The CEF emissions from wholesale and retail, construction, and urban residential sectors share similar spatial distributions, primarily concentrated in major cities within the region and their surrounding areas. Agricultural CEF emissions are mainly concentrated in southern Jiangsu. Given this sectoral heterogeneity, targeted emission reduction policies can be developed. For instance, regions where emissions are concentrated in the industrial sector may benefit from investments in clean technology and energy efficiency. Similarly, areas with high transportation emissions could focus on improving public transportation infrastructure or incentivizing the adoption of electric vehicles.”

Comments 11: In addition, the Conclusion Section may also be able to more clearly point out the practical application value and policy implications of the research.

Response11: Thank you for your valuable feedback. We have enhanced the conclusion to stress the practical applications of our findings, detailing how regional emissions data can be utilized. The original text follows:

“To achieve regional sustainable development, local governments must implement effective intervention policies. The carbon emission measurements for the Yangtze River Delta region from 2000 to 2020 reveal that carbon emissions exhibit significant spatial spillover, evolving from a single growth pole to a multi-center trend. Since car-bon emissions are not confined by administrative boundaries, inter-regional cooperation is crucial. To meet the central government's "dual carbon" goals, the Yangtze River Delta should leverage its resource advantages and promote coordinated governance for pollution and carbon reduction within a regional integration framework. For the existing carbon emission growth pole, Shanghai, it is crucial to focus on and monitor its emissions to prevent further diffusion, while also closely observing emerging secondary growth poles such as Nanjing, Hefei, Yangzhou, and Ningbo. Efforts should be made to accelerate the construction of low-carbon cities in these areas to prevent an accelerated increase in carbon emission growth rates. In addition to requiring close cooperation between regions, different regions should implement appropriate policies based on their unique characteristics to mitigate carbon emissions. Tailored emission reduction measures can be proposed based on the carbon emissions of different sectors. For industrial carbon emissions centers such as Shanghai, Suzhou, Jiaxing, Hangzhou, Shaoxing, and Ningbo, the promotion of clean energy should be prioritized. Efforts should be made to create green industrial parks and encourage enterprises to transition to greener technologies through tax subsidies and pollution control measures. For regions with high transportation-related carbon emissions, such as Nanjing, Hefei, Suzhou, Hangzhou, Yangzhou, and Wuxi, public transportation infrastructure should be expanded, and the substitution of traditional fuel-powered vehicles with new energy vehicles should be accelerated. Additionally, efforts should be made to reduce reliance on private cars by improving public transportation facilities. In residential areas, subsidies for new energy vehicles should be increased to encourage residents to scrap older, high-emission vehicles. Incentives, such as discounts on public transportation fares, should also be provided to encourage residents to choose more environmentally friendly modes of transportation. For high carbon emission wholesale and retail sectors in regions such as Shanghai, Hangzhou, Wuxi, and Hefei, local governments should encourage the production and sale of goods locally, shorten supply chains, and establish energy-efficient factories and warehouses to reduce emissions along the supply chain. For regions with high carbon emissions in the construction industry, such as Shanghai, Hangzhou, Hefei, and Suzhou, governments should consider introducing green building certification systems. This would encourage the use of low-carbon materials and energy-efficient construction practices to minimize energy consumption and maximize the use of renewable energy, providing the most comfortable indoor environments with the least energy consumption. In southern Jiangsu, where there are significant agricultural carbon emissions, efforts should be made to reduce the proportion of land used for field embankments, increase the level of agricultural mechanization, and minimize unnecessary fertilizer use. This would enhance agricultural productivity and reduce the carbon emission intensity of the agricultural sector. For cities and rural areas with high carbon emissions, such as Shanghai, Hangzhou, Nanjing, Hefei, and Suzhou, sustainable urbanization should be planned carefully. Urban sprawl should be avoided, and efforts should be made to guide residents to concentrate in specific areas to create economies of scale. Green infrastructure should be laid in densely populated areas to reduce carbon emissions while improving residents' quality of life.”

Comments 12: The aesthetics of the Figures in this manuscript need to be improved, and the font size also needs to be increased.

Response12: Thank you for your valuable suggestions. We have updated the figures and tables in the article to accurately reflect the changes in the data.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Overall this is a well written paper with a strong methodological section and clear results. I feel the topic - a further refinement at a local level of NTL emissions data has strong academic and policy benefits. In the Ln comments below I have highlighted a few minor points which I feel would further enhance readability/presentation

I only have one structural concern with the paper. At the end of section 2 - lns 188-191 you state what you have achieved with this paper - something I would agree that you meet. However, in lines 95-101 you set 4 objectives that you are looking to achieve in presenting this work. I found it difficult to align these with comments in the results or discussion, which are more focused on the methodology and analysis of effectiveness. The final section of the conclusion does suggest some targetted interventions which may be better indicated earlier in the discussion?I wonder if it would be helpful to either rethink your objectives or incorporate them into the results/discussion in a more structured way?

Minor comments

ln 22 - CO2 - general preference if for CO₂

Ln 22 - CEF - standard practice is to present description in full before abbreviation - this also appears in other parts of the paper

Ln 35-37 - this sentence feels clumsy - can you make this clearer?

Ln 35 - there should be a gap between the word and bracket for all references

Ln 78-79 - Use existing.........is not a sentence

Ln93 - as you are presenting a model - GTWR I wonder if you should reference Fotheringham, Crespo and Yao - 2015?

Ln 103 - I would suggest removing the word 'marginal'

Ln 119 - I feel you should reference the IPCC

ln 126 - the readability of the sentence may be improved if it read - ........China only disclosed these at the provincial level.

ln 130 - when you talk about the early stages - is this of model development?

Ln 149 - I wonder if you would describe DMSP-NPP as measures? Do you feel they are well enough recognised without having to at least write out in full the first time?

ln 169-171 - this sentence feels like a repetition of a point made earlier?

ln 178 - comma after for carbon emissions,

Ln 179 - should the start of this sentence read - In more rather than for more?

ln 203 - the visuals are too small to read the legend clearly - also what may seem a very small point but you have not labelled map 1 as 'China'. Also it is not clear what the legend on the second map refers to - ie planning scope - this does not seem to be referenced in the copy.

ln 231 - please could you reference China energy Statitical Yearbook

ln 246 -Figure 2 is unreadable - the text is too small.

ln 287 - again we would suggest referencing this source

ln 419 - again legend too small to read - same for all remaining figures

ln 487 - I understand the meaning of this last sentence but 'backward industries' is an unusual phrase - can you make this clearer. You may also want to reconsider the use of the word 'results'.

Lns 556-558 - suggest references to sources

Author Response

Dear Editor:

Responses to the reviewer‘s comments:

Reviewer #2:

Response: Thank you very much for your positive feedback on the manuscript. We appreciate your valuable input and will incorporate your minor suggestions to improve the paper's readability and presentation.

Comments 1: I only have one structural concern with the paper. At the end of section 2 - lns 188-191 you state what you have achieved with this paper - something I would agree that you meet. However, in lines 95-101 you set 4 objectives that you are looking to achieve in presenting this work. I found it difficult to align these with comments in the results or discussion, which are more focused on the methodology and analysis of effectiveness. The final section of the conclusion does suggest some targetted interventions which may be better indicated earlier in the discussion? I wonder if it would be helpful to either rethink your objectives or incorporate them into the results/discussion in a more structured way?

Response1: Thank you for your valuable feedback. We appreciate your observation that the objectives presented in lines 95-101 could be better aligned with the results and discussion sections. We agree that ensuring a clear connection between the objectives and the main findings would enhance the overall clarity and coherence of the paper.

To address this, we have revised the Discussion section We emphasize the policy implications of our study, particularly how spatial emissions data can aid decision-makers in developing low-carbon strategies. Additionally, we have strengthened the Conclusion to highlight the practical applications of our findings and detail the use of regional emissions data. The original text follows:

“To achieve regional sustainable development, local governments must implement effective intervention policies. The carbon emission measurements for the Yangtze River Delta region from 2000 to 2020 reveal that carbon emissions exhibit significant spatial spillover, evolving from a single growth pole to a multi-center trend. Since carbon emissions are not confined by administrative boundaries, inter-regional cooperation is crucial. To meet the central government's "dual carbon" goals, the Yangtze River Delta should leverage its resource advantages and promote coordinated governance for pollution and carbon reduction within a regional integration framework. For the existing carbon emission growth pole, Shanghai, it is crucial to focus on and monitor its emissions to prevent further diffusion, while also closely observing emerging secondary growth poles such as Nanjing, Hefei, Yangzhou, and Ningbo. Efforts should be made to accelerate the construction of low-carbon cities in these areas to prevent an accelerated increase in carbon emission growth rates. In addition to requiring close cooperation between regions, different regions should implement appropriate policies based on their unique characteristics to mitigate carbon emissions. Tailored emission reduction measures can be proposed based on the carbon emissions of different sectors. For industrial carbon emissions centers such as Shanghai, Suzhou, Jiaxing, Hangzhou, Shaoxing, and Ningbo, the promotion of clean energy should be prioritized. Efforts should be made to create green industrial parks and encourage enterprises to transition to greener technologies through tax subsidies and pollution control measures. For regions with high transportation-related carbon emissions, such as Nanjing, Hefei, Suzhou, Hangzhou, Yangzhou, and Wuxi, public transportation infrastructure should be expanded, and the substitution of traditional fuel-powered vehicles with new energy vehicles should be accelerated. Additionally, efforts should be made to reduce reliance on private cars by improving public transportation facilities. In residential areas, subsidies for new energy vehicles should be increased to encourage residents to scrap older, high-emission vehicles. Incentives, such as discounts on public transportation fares, should also be provided to encourage residents to choose more environmentally friendly modes of transportation. For high carbon emission wholesale and retail sectors in regions such as Shanghai, Hangzhou, Wuxi, and Hefei, local governments should encourage the production and sale of goods locally, shorten supply chains, and establish energy-efficient factories and warehouses to reduce emissions along the supply chain. For regions with high carbon emissions in the construction industry, such as Shanghai, Hangzhou, Hefei, and Suzhou, governments should consider introducing green building certification systems. This would encourage the use of low-carbon materials and energy-efficient construction practices to minimize energy consumption and maximize the use of renewable energy, providing the most comfortable indoor environments with the least energy consumption. In southern Jiangsu, where there are significant agricultural carbon emissions, efforts should be made to reduce the proportion of land used for field embankments, increase the level of agricultural mechanization, and minimize unnecessary fertilizer use. This would enhance agricultural productivity and reduce the carbon emission intensity of the agricultural sector. For cities and rural areas with high carbon emissions, such as Shanghai, Hangzhou, Nanjing, Hefei, and Suzhou, sustainable urbanization should be planned carefully. Urban sprawl should be avoided, and efforts should be made to guide residents to concentrate in specific areas to create economies of scale. Green infrastructure should be laid in densely populated areas to reduce carbon emissions while improving residents' quality of life.”

Comments 2: Minor comments

Response2: Thank you for your helpful comments. I have made the revisions based on your suggestions. These revisions address the minor issues raised and should help improve the overall clarity and consistency of the manuscript. Thank you again for your thorough review and valuable suggestions.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Please address the comments you can find in the attached file.

Comments for author File: Comments.pdf

Author Response

Dear Editor:

Responses to the reviewer‘s comments:

Reviewer #3: This paper attempts to use nighttime light satellite images in combination with land use data in order to evaluate the intensity and sources of carbon dioxide emissions in China at a county level in the Yangtze river delta in order to inform government policymakers in the environmental area. As the authors combine data on light intensity from different sources, they use neural networks for the purpose of harmonization, which is a commendable and interesting approach. The authors come up with a series of carbon emissions’ estimates at a county level breaking them down by sectors of the economy. Their main conclusion seems to be that concerted efforts are needed to deal with the problem of increased carbon emissions because of the existence of spatial spillover effects.

The study is contributing to the existing literature provided it is the first to come up with a detailed breakdown of carbon emissions by county and economic sector in the Yang-Tze Delta Region. However, the policy implication that the local governments should take a coordinated approach to dealing with the emissions problem is rather obvious and is not directly supported by the authors’ analysis.

This paper is unduly abundant in all kinds of technical estimates while lacking in research focus. I would recommend this paper for publication in case the authors’ emissions estimates are indeed helpful in light of their ability to complement the already existing databases. In this case the authors are well advised to address comments below.

Response: Thank you very much for taking the time to review this manuscript. Below are our detailed responses and the corresponding revisions highlighted in the resubmitted files.

Comments 1: The title is difficult to understand as there are too many modifiers. Think about “multi-scale mapping of the spatiotemporal characteristics of carbon emissions caused by energy consumption”, but even that is probably too long. Make the title more concise and informative.

Response1: Thank you for your valuable feedback on the title. I have revised it to be clearer while maintaining the study's core focus. The new title is:

“Spatiotemporal Analysis of Energy Consumption Carbon Emissions by Industry: A Case Study of the Yangtze River Delta”

I hope this revision matches your suggestion for a more concise title, accurately reflecting the study's content and scope.

Comments 2: In the Introduction section, explain what counties are in the Chinese context, in particular relative to their US analogues.

Response2: Thank you for your insightful comment. To address this, I have revised the Introduction section to explain the role and structure of counties in China, and I have made a comparison to their U.S. analogs for better context. The original text follows:

“ In China, the administrative hierarchy features several levels, with the county serving as a crucial unit of local governance. Unlike U.S. counties, which mainly function as political subdivisions of a state, Chinese counties are directly involved in local administration. U.S. counties primarily handle local services and enforce state laws, while Chinese counties focus on grassroots governance and services. These differences in structure and function are important to understand when studying carbon emissions and economic development at the county level in China, as they reflect the unique governance system that influences local decision-making and policy implementation. We can achieve a more detailed understanding of emission patterns at the county level, enabling targeted analysis that surpasses broader provincial or municipal assessments.”

Comments 3: Explain acronyms as soon as you mention them for the first time: for instance, what’s GTWR on line 93 in the Introduction? Line 108, CEF—what is it?

Response3: Thank you for your valuable feedback. To address this, we have clarified the abbreviations introduced in the article.

Comments 4: The Introduction section motivates the analysis well enough but then in the last paragraph of this section when the authors list their contributions, it should be done in a more concise manner. In its present form the last paragraph is more of a discussion than a list of contributions. Additionally, the authors should explain better in general why being able to measure carbon emissions at a county level while differentiating them by their source is such a useful idea.

Response4: Thank you for your helpful feedback. We have revised the contributions section to ensure it is more succinct and clear. Additionally, we have added further clarification on the practical importance of measuring carbon emissions at the county level while differentiating by source. This distinction allows for more localized, sector-specific strategies, making emission reduction efforts more targeted and effective. The original text follows:

“Our present study offers several marginal contributions to this field of research: (1) We combine nighttime light (NTL) data with land use information to simulate carbon emissions, improving accuracy over single-method approaches. This method addresses data limitations, offering more precise regional estimates. (2) In contrast to previous studies on total CO2 emissions, we assess carbon emissions by industry sector using the NTL-Landuse framework. This multi-scale approach offers insights into sector-specific emission patterns, allowing for more targeted policy interventions. (3) This study tests the model in the Yangtze River Delta, examining carbon emissions trends and suggesting region-specific reduction policies.”

Comments 5: In the Literature Review section the authors often repeat themselves. Thus, they mention multiple times that the detailed data on carbon emissions are not available at a city or county level in China. It’s OK to say this just once, say, in the first paragraph of the section.

Response5: Thank you for your valuable feedback. To address this, I have revised the Literature Review to mention this issue only once, in the first paragraph. The later sections now concentrate more on discussing existing research and methodologies without repeating this point.

Comments 6: The biggest problem with the Literature Review section is it doesn’t explain how combining the nighttime light satellite images with the land usage data helps estimate and differentiate carbon emission levels by industrial sectors.

Response6: Thank you for your valuable suggestions. In response to your questions on this and the previous one, we have significantly revised the structure and content of the literature review chapter to better reflect the current research process. The original text follows:

Comments 7: Section 3 is rather messy and looks more like a collection of pieces describing different methodologies without an explanation of what the authors are going to do with them.

Response7: Thank you for your valuable feedback. We appreciate your observation that Section 3 currently appears disorganized and reads more like a collection of methodologies without a clear explanation of how they are applied in the study.

To address this, we will restructure Section 3 to clearly outline the methodologies and their specific roles in the analysis. First, we deleted the first paragraph of Section 3. Second, we merged Section 3.3 with Section 3.4. Finally, we also made revisions to the specific content to avoid unnecessary ambiguity. We will ensure that each methodology is introduced with an explanation of how it contributes to the overall research objectives and analysis. This will provide a clearer roadmap for readers, allowing them to better understand the logical progression and how the methodologies are integrated into the study.

Comments 8: In Section 4 the authors refer to the results of Table 2 to argue that, since the correlation coefficients between NTL and land use are higher compared to correlation coefficients with NTL-Total, it is better to use NTL-Landuse indicators for their analysis. However, the difference between two correlation coefficients does not appear to be very impressive. How important is this difference on any objective grounds?

Response8: Thank you for your valuable feedback. Table 2 results indicate that correlation coefficients between NTL-Landuse and carbon emissions across sectors are generally higher than those for NTL-Total. This suggests that NTL-Landuse has a stronger linear correlation with sectoral carbon emissions. Importantly, the main advantage of using NTL-Landuse is its capacity to estimate sectoral carbon emissions with similar or even greater precision, emphasizing the rationale behind variable selection rather than an enhancement in accuracy. We have revised the phrasing in this section to avoid any potential misunderstandings. The original text follows:

“We need to compare the correlation coefficients between the NTL-Landuse and NTL-Total datasets and the carbon emissions across various sectors to select the appropriate independent variables. From a theoretical perspective, sectoral carbon emissions are closely related to land use patterns (e.g., agricultural land tends to have more agricultural carbon emissions rather than industrial or construction-related emissions). The correlation between the matched NTL and CEF is shown in Table 2. By comparing with the correlation results of NTL-Total, it can be observed that, except for the agricultural sector, NTL-Landuse has higher correlation coefficients with CEF in all sectors. Although the difference in correlation coefficients between NTL-Landuse and NTL-Total for CEF is small, we still have reasons to believe that using NTL-Landuse can improve the accuracy of CEF estimation. Therefore, using NTL-Landuse as an independent variable is appropriate. Secondly, comparing the correlation coefficients of different sectors with NTL-Landuse data reveals that direct sectors exhibit coefficients ranging between [0.8, 1], indicating a high linear correlation. This suggests that NTL-Landuse effectively captures the CEF of these sectors. In contrast, indirect sectors have correlation coefficients between [0.6, 0.8], lower than those of direct sectors but still within a range of significant linear correlation. The existence of differences in correlation coefficients between NTL-Landuse and NTL-Total for CEF also indicates that the aggregated NTL neglects the sectoral heterogeneity when estimating carbon emissions. Therefore, incorporating NTL-Landuse will help identify this sectoral heterogeneity. This is especially important in regions where land use types have distinct structures, as the use of NTL-Landuse will be crucial for identifying CEF. Additionally, relying solely on NTL-Total cannot accurately assess the spatial distribution of sectoral CEF, as both are estimated based on the same nightlight distribution, which can introduce biases when mapping the spatial distribution of sectoral CEF. In such cases, even a slight improvement in accuracy makes the choice of NTL-Landuse for CEF estimation significant for enhancing the precision of identifying emission spatial distribution.”

Comments 9: In Section 4.2, what exact regression specification are the authors referring to exactly?

Response9: Thank you very much for your valuable feedback. In the revised manuscript, we will include the full regression specification, clearly stating the mathematical form of the model. This will ensure that the model is fully defined and reproducible by other researchers. We will also include a brief justification for the choice of the regression model, explaining why it was the most appropriate for the objectives of the study. In order to maintain a reasonable structure, we added this part to 3.4.2. The original text follows:

The specific regression model for GTWR is as follows:

Comments 10: Section 4.3 presents an interesting map of carbon emissions by counties in the study region. However, what is the breakdown by industries within those counties? If this kind of estimation was not initially the authors’ intent, they should make it clear that the idea is to come up with a map of carbon emissions by small administrative units rather than industries within these units.

Response10: Thank you for your insightful comment on the map presented in Section 4.3. This section focuses on providing a spatial overview of carbon emissions at the county level, rather than offering a detailed sectoral breakdown within these counties. We have therefore emphasized this point in the revised manuscript to ensure that readers can better understand the topic of this subsection. The original text follows:

“Using NTL-Landuse as the independent variable input into the GTWR model yielded the estimation results of CEF. To observe the spatial heterogeneity of carbon emissions at the county level, we visualized the calculated CEF using ArcGIS (see Figure 3). The resulting map provides a spatial distribution of CEF at the county level across the study region. This map does not distinguish between emissions from specific industries within these counties but rather illustrates the overall carbon emissions at the county scale.

The spatial distribution of CEF shows distinct patterns. Low CEF values are primarily observed in the northeast, northwest, and most rural areas of the central region. In contrast, high CEF values are predominantly concentrated in developed urban areas, where industrial activities, dense populations, and vigorous economic activities contribute to high energy consumption and CO2 emissions. Notably, the Yangtze River Delta region stands out for its high CEF, which results from the concentration of energy-intensive industries, a large population, and a dynamic economy. This is also the reason why we chose the Yangtze River Delta as our research area. By comparing the spatial distribution of CEF in the Yangtze River Delta region in different periods, it can be found that carbon emissions show a clear upward trend. Specifically, Specifically, CEF in Zhejiang Province increased by 281.19%, in Shanghai Municipality by 83.38%, in Jiangsu Province by 327.93%, and in Anhui Province by 228.85%.”

Comments 11: Section 4.5, first paragraph: the authors don’t seem to understand the concept of a right-skewed distribution. Figure 5 that is supposed to corroborate the authors’ statement is a bi-dimensional spatial distribution graph, while a right-skewed distribution is a density function whose mode is on the left side of the density function graph, with a long right tail. What did the authors mean then by a “right-skewed distribution”??

Response11: Thank you very much for your valuable feedback. Our understanding of the data distribution is not solely based on Figure 5, which merely visualizes the kernel density. We calculated skewness for each year and created corresponding kernel density plots, but due to space limitations, these were not included in the paper; instead, we presented the results directly. This omission may have caused misunderstandings. To clarify, we have offered a more detailed explanation of this conclusion. The original text follows:

“From 2000 to 2020, the CEF in the study area initially exhibited a bimodal distribution (See appendix). Apart from the low peak, a small high peak area formed in Shanghai and its surrounding areas. As the scale of CEF emissions continues to expand across regions, the bimodal distribution gradually shifts to an unimodal distribution. The kernel density curve shows a rightward shift, and although it still exhibits a right-skewed distribution, the skewness value has decreased year by year, indicating a consistent increase in CEF. Meanwhile, the peak of the CEF kernel density has shown a declining trend, suggesting that the data distribution is becoming more dispersed. Specific to the performance in space, Figure 5 clearly shows that Shanghai is developing into a high CEF emission center, which gradually expands to surrounding areas, resulting in a higher proportion of high CEF regions.”

Appendix. 2. Kernel Density Plot

Comments 12: In Section 4.6 (1st paragraph) the authors start discussing Moran’s I index without providing any description of what it is and what it is used for. A short paragraph explaining this measure of spatial correlation would be useful for a general reader. Also, what does “Moran’s I index of urbanization” mean? Similarly, the concept of hotspots is not explained. In general, Section 4.6 provides a statistical description of the developments in carbon emissions in different counties and industries, but what is the general message of this analysis? Emissions kept increasing? But we know that already.

Response12: Thank you for your valuable suggestions. We briefly describe the Moran’s I index and hot spot analysis to ensure that readers can quickly understand the research topic of this section. The original text follows:

“Spatial correlation is primarily assessed using the Moran's I index. When Moran's I is greater than 0, it indicates positive spatial autocorrelation within the region; when Moran's I is less than 0, it indicates negative spatial autocorrelation within the region; when Moran's I equals 0, it suggests the data exhibit random spatial distribution characteristics within the region.”

“Hotspot analysis is a form of local autocorrelation analysis. It calculates a statistic for each feature in the dataset and provides an evaluation of whether its surrounding environment forms a hotspot or a cold spot. A hotspot signifies high values in both the area and its surroundings, while a cold spot indicates low values in both.”

Comments 13: Figure 7 is terrible: there are four spatial distribution graphs with no captions at all. What do these graphs imply and how are they different from each other? Are they estimations for different years, for instance?

Response13: Thank you for your valuable suggestions. We added a subtitle to Figure 7 to improve its readability. At the same time, we also beautified other charts to facilitate readers' reading.

Comments 14: Specific Comments

Response 14: Thank you for your helpful comments. I have made the revisions based on your suggestions. These revisions address the minor issues raised and should help improve the overall clarity and consistency of the manuscript. Thank you again for your thorough review and valuable suggestions.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Thank you for the opportunity to read and review such an interesting and well-prepared article. The article presents research on carbon dioxide emissions on a regional example, but the methods used can also be applied to other areas. This is definitely a strong point of the research. Another strong point of the research is its utilitarian nature - it not only contributes to the development of science, but also has a dimension that can be used in practice.

Detailed comments:

The manuscript is clear and relevant for the field. The text is presented in a well-structured manner. The article is divided into sections that correspond to the structure preferred by the journal, as well as the requirements of a scientific text. In addition, the separated subsections affect the clarity of the text and ease of navigation, which facilitates understanding.
The manuscript contains many references.The cited references are mostly recent publication and relevant for the field. This proves that the research was well prepared based on the literature.
The manuscript is scientifically sound and experimental design is appropriate. There is no information about the research hypotheses tested in the study. It is worth supplementing the text with hypotheses.
The manuscript’s results are reproducible based on the details given in methods section. The methods section is presented in detail. The proposed methods can be applied to data from other regions, which is definitely a strong point of the paper. These methods can also be developed in the future.
Tables are appropriate and properly show the data. They are easy to interpret and understand. Figures are also necessary, but some of them contain too much detail (or are too small). It is worth correcting this because it contains important data. In particular, figures 2 and 4.
The conclusions are consistent with the evidence and arguments presented. The conclusions are very important, they also contain managerial implications.
The discussion section contains too few references to research by other authors mentioned in the introduction and literature review.
It is worth adding Yangtze River Delta Region to your keywords.
The abstract is too long. According to the journal's guidelines, the abstract should not exceed 200 words.
Research objectives in the introduction: the objective (4) should include evaluation, not analysis. Analysis cannot be the objective, because it is a tool. It is also worth indicating the main objective. At the end of the section "literature review" the objective/ aim is also given. Which formulation is the most important? Which aim/ objective is the main?
The first paragraph of section 3 is unnecessary, it is better to delete it and start the title of subsection 3.1 with a capital letter.
The style of the reference list is not adapted to the journal's requirements. It is worth correcting this.

Godd luck!

Author Response

Dear Editor:

Responses to the reviewer‘s comments:

Reviewer #4:

Response: Thank you very much for your thoughtful and encouraging feedback. We are grateful for your recognition of the strengths of our research, particularly the regional applicability of the methods, as well as the practical relevance of the study. We appreciate your constructive feedback as it helps us refine and improve the paper.

Comments 1: The manuscript is clear and relevant for the field. The text is presented in a well-structured manner. The article is divided into sections that correspond to the structure preferred by the journal, as well as the requirements of a scientific text. In addition, the separated subsections affect the clarity of the text and ease of navigation, which facilitates understanding.

Response 1: Thank you very much for your positive feedback on the clarity and structure of the manuscript. We greatly appreciate your acknowledgment of how the separated subsections enhance the readability and navigation of the article. This feedback is encouraging and reinforces our approach to presenting the content in a clear and accessible manner.

Comments 2: The manuscript contains many references. The cited references are mostly recent publication and relevant for the field. This proves that the research was well prepared based on the literature.

Response 2: Thank you for your positive comments regarding the references used in the manuscript. We are glad that the literature review effectively supports the foundation of the study and demonstrates the thoroughness of our preparation. We updated the 2024 related literature and included previously unmentioned sources to better reflect current research. The new references are as following:

Gao, F., Wu, J., Xiao, J., Li, X., Liao, S., & Chen, W. (2023). Spatially explicit carbon emissions by remote sensing and social sensing. Environmental Research, 221, 115257.

Liu, C., Hu, S., Wu, S., Song, J., & Li, H. (2024). County-level land use carbon emissions in China: spatiotemporal patterns and impact factors. Sustainable Cities and Society, 105304.

Comments 3: The manuscript is scientifically sound and experimental design is appropriate. There is no information about the research hypotheses tested in the study. It is worth supplementing the text with hypotheses.

Response 3: Thank you for your constructive feedback regarding the inclusion of research hypotheses. We appreciate your suggestion to explicitly outline the hypotheses tested in the study. In response, we will revise the manuscript to clearly state the hypotheses that guided our research. We believe this addition will further strengthen the clarity and depth of the manuscript. The original text follows:

Comments 4: The manuscript’s results are reproducible based on the details given in methods section. The methods section is presented in detail. The proposed methods can be applied to data from other regions, which is definitely a strong point of the paper. These methods can also be developed in the future.

Response 4: Thank you for your positive feedback regarding the clarity and reproducibility of the methods section. We appreciate the detailed results that enable reproducibility and the applicability of the methods to data from other regions. We agree that this flexibility is a key strength of the study and are pleased you recognize their potential for future development and broader application. We will continue to explore enhancements and adaptations in our future research.

Comments 5: Tables are appropriate and properly show the data. They are easy to interpret and understand. Figures are also necessary, but some of them contain too much detail (or are too small). It is worth correcting this because it contains important data. In particular, figures 2 and 4.

Response 5: Thank you for your valuable suggestions. We have updated the figures and tables in the article to accurately reflect the changes in the data.

Comments 6: The conclusions are consistent with the evidence and arguments presented. The conclusions are very important, they also contain managerial implications.

Response 6: Thank you for your valuable feedback. We have enhanced the conclusion to stress the practical applications of our findings, detailing how regional emissions data can be utilized. The original text follows:

In addition to requiring close cooperation between regions, different regions should implement appropriate policies based on their unique characteristics to mitigate carbon emissions. Tailored emission reduction measures can be proposed based on the carbon emissions of different sectors. For industrial carbon emissions centers such as Shanghai, Suzhou, Jiaxing, Hangzhou, Shaoxing, and Ningbo, the promotion of clean energy should be prioritized. Efforts should be made to create green industrial parks and encourage enterprises to transition to greener technologies through tax subsidies and pollution control measures. For regions with high transportation-related carbon emissions, such as Nanjing, Hefei, Suzhou, Hangzhou, Yangzhou, and Wuxi, public transportation infrastructure should be expanded, and the substitution of traditional fuel-powered vehicles with new energy vehicles should be accelerated. Additionally, efforts should be made to reduce reliance on private cars by improving public transportation facilities. In residential areas, subsidies for new energy vehicles should be increased to encourage residents to scrap older, high-emission vehicles. Incentives, such as discounts on public transportation fares, should also be provided to encourage residents to choose more environmentally friendly modes of transportation. For high carbon emission wholesale and retail sectors in regions such as Shanghai, Hangzhou, Wuxi, and Hefei, local governments should encourage the production and sale of goods locally, shorten supply chains, and establish energy-efficient factories and warehouses to reduce emissions along the supply chain. For regions with high carbon emissions in the construction industry, such as Shanghai, Hangzhou, Hefei, and Suzhou, governments should consider introducing green building certification systems. This would encourage the use of low-carbon materials and energy-efficient construction practices to minimize energy consumption and maximize the use of renewable energy, providing the most comfortable indoor environments with the least energy consumption. In southern Jiangsu, where there are significant agricultural carbon emissions, efforts should be made to reduce the proportion of land used for field embankments, increase the level of agricultural mechanization, and minimize unnecessary fertilizer use. This would enhance agricultural productivity and reduce the carbon emission intensity of the agricultural sector. For cities and rural areas with high carbon emissions, such as Shanghai, Hangzhou, Nan-jing, Hefei, and Suzhou, sustainable urbanization should be planned carefully. Urban sprawl should be avoided, and efforts should be made to guide residents to concentrate in specific areas to create economies of scale. Green infrastructure should be laid in densely populated areas to reduce carbon emissions while improving residents' quality of life.”

Comments 7: The discussion section contains too few references to research by other authors mentioned in the introduction and literature review.

Response 7: Thank you for your valuable feedback. We appreciate your observation that the discussion section lacks sufficient references to research by other authors. In response to your suggestion, we will revise the discussion section to incorporate more references to prior research. The references are as following:

Chen, G. Q., & Chen, Z. M. (2010). Carbon emissions and resources use by Chinese economy 2007: a 135-sector inventory and input–output embodiment. Communications in Nonlinear Science and Numerical Simulation, 15(11), 3647-3732.

Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., ... & Shan, Y. (2020). County-level CO2 emissions and sequestration in China during 1997–2017. Scientific data, 7(1), 391.

Chen, X., Di, Q., & Liang, C. (2024). The mechanism and path of pollution reduction and carbon reduction affecting high quality economic development-taking the Yangtze River Delta urban agglomeration as an example. Applied Energy, 376, 124340.

Cui, Y., Khan, S. U., Deng, Y., & Zhao, M. (2022). Spatiotemporal heterogeneity, convergence and its impact factors: Per-spective of carbon emission intensity and carbon emission per capita considering carbon sink effect. Environmental Impact Assessment Review, 92, 106699.

Hu, K., Liu, Z., Shao, P., Ma, K., Xu, Y., Wang, S., ... & Zhang, Y. (2024). A review of satellite-based CO2 data reconstruction studies: methodologies, challenges, and advances. Remote Sensing, 16(20).

Lv, T., Hu, H., Zhang, X., Xie, H., Fu, S., & Wang, L. (2022). Spatiotemporal pattern of regional carbon emissions and its influencing factors in the Yangtze River Delta agglomeration of China. Environmental Monitoring and Assessment, 194(7), 515.

Shan, Y., Guan, Y., Hang, Y., Zheng, H., Li, Y., Guan, D., ... & Hubacek, K. (2022). City-level emission peak and drivers in China. Science Bulletin, 67(18), 1910-1920.

Wu, W., Zhang, T., Xie, X., & Huang, Z. (2021). Regional low carbon development pathways for the Yangtze River Delta region in China. Energy Policy, 151, 112172.

Zhao, J., Ji, G., Yue, Y., Lai, Z., Chen, Y., Yang, D., ... & Wang, Z. (2019). Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets. Applied Energy, 235, 612-624.

Comments 8: It is worth adding Yangtze River Delta Region to your keywords.

Response 8: Thank you for your valuable suggestions. We have added the Yangtze River Delta region as a keyword in the revised manuscript

Comments 9: The abstract is too long. According to the journal's guidelines, the abstract should not exceed 200 words.

Response 9: Thank you for your valuable suggestions. We will revise the abstract to meet the required length while ensuring that it clearly conveys the study's objectives, methodology, and key findings. Below is a revised version of the abstract:

“Abstract: Climate issues significantly impact people's lives, prompting governments worldwide to implement energy-saving and emission-reducing measures. However, many areas lack carbon emission data at the lower administrative divisions. Additionally, the inconsistency in the standards, scope, and accuracy of carbon dioxide emission statistics across different regions makes mapping carbon dioxide spatial patterns complex. Nighttime light (NTL) data combined with land use data enables the detailed spatial and temporal disaggregation of carbon emission data at a finer administrative level, facilitating scientifically informed policy formulation by the government. Carbon emission data by sector will help us further identify the carbon emission efficiency in different sectors and help environmental regulators implement the most cost-effective emission reduction strategy. This study uses integrated remote sensing data to estimate carbon emissions from fossil fuels (CEF). Experimental results indicate, (1) The regional CEF can be calculated by combining NTL and Landuse data, and has a good fit; (2) The high-intensity CEF area is mainly concentrated in Shanghai and its surrounding areas, showing a concentric circle structure; (3) There are obvious differences in the spatial distribution characteristics of carbon emissions among different departments; (4) Hot spot analysis reveals a three-tiered distribution in the Yangtze River Delta, increasing from west to east with distinct spatial characteristics.”

Comments 10: Research objectives in the introduction: the objective (4) should include evaluation, not analysis. Analysis cannot be the objective, because it is a tool. It is also worth indicating the main objective. At the end of the section "literature review" the objective/ aim is also given. Which formulation is the most important? Which aim/ objective is the main?

Response 10: Thank you for your valuable suggestions. We have adopted your suggestion and modified the wording of Objective 4. At the same time, we have deleted the description of the research purpose at the end of the literature review to avoid unnecessary ambiguity.

Comments 11: The first paragraph of section 3 is unnecessary, it is better to delete it and start the title of subsection 3.1 with a capital letter.

Response 11: Thank you for your valuable suggestions. We have deleted the first paragraph of Section 3 and started the heading of Subsection 3.1 with capital letters.

Comments 12: The style of the reference list is not adapted to the journal's requirements. It is worth correcting this.

Response 12: Thank you for pointing out the issue with the reference list style. We appreciate your attention to detail. We have revised the reference list to ensure that it adheres to the journal's formatting requirements.

Author Response File: Author Response.docx

Reviewer 5 Report

Comments and Suggestions for Authors

1. Page 1, line 14. What kind of precise monitoring do the authors mean that will help other countries.

2. Page 1, line 16. The authors should specify exactly what kind of inconsistency of statistical models they are talking about. It is advisable for the authors to change the context of the first sentences of the abstract to justify the importance of night lighting for accurate mapping to reduce decarbonization with effective land use in individual regions with their specifics of industrial development.

3. Page 12, line 270. Requires explanation according to the emission factor.

4. It is advisable for the authors to justify why road transport emissions are not divided into diesel and gasoline, because there is a significant difference in this problem.

Author Response

Dear Editor:

Responses to the reviewer‘s comments:

Reviewer #5:

Comments 1: Page 1, line 14. What kind of precise monitoring do the authors mean that will help other countries.

Response 1: Thank you for your valuable feedback. To avoid ambiguity, we have rewritten this sentence to hopefully make it clearer. The original text follows:

“Nighttime light (NTL) data combined with land use data enables the detailed spatial and temporal disaggregation of carbon emission data at a finer administrative level, facilitating scientifically informed policy formulation by the government.”

Comments 2: Page 1, line 16. The authors should specify exactly what kind of inconsistency of statistical models they are talking about. It is advisable for the authors to change the context of the first sentences of the abstract to justify the importance of night lighting for accurate mapping to reduce decarbonization with effective land use in individual regions with their specifics of industrial development.

Response 2: Thank you for your valuable suggestions. According to your suggestions, we adjusted the structure of the abstract and simplified its length to make it more concise and clear in expressing the study's objectives, methodology, and key findings. Below is a revised version of the abstract:

Comments 3: Page 12, line 270. Requires explanation according to the emission factor.

Response 3: Thank you for your valuable feedback. We will revise the manuscript to include a more detailed explanation of how the emission factor is derived and used in this study. The original text follows:

“According to the IPCC method, the Emission factor (EF) can be expressed as (NCV*CC*O*44/12). The emission factor represents the amount of CO2 emitted per unit of activity or energy consumed.”

Comments 4: It is advisable for the authors to justify why road transport emissions are not divided into diesel and gasoline, because there is a significant difference in this problem.

Response 4: Thank you for your valuable feedback. We acknowledge that there may be significant differences between diesel and gasoline-powered transportation. However, the key constraint in our analysis is the lack of classified data on the specific contributions of diesel and gasoline vehicles, making it difficult to distinguish between the two sources within the transportation sector. Therefore, while we recognize the importance of differentiating fuel types, we adopted a simplified approach to maintain the robustness of the model and avoid potential errors that could arise from excessive precision due to data limitations. In the revised manuscript, we will acknowledge this limitation and suggest that future research should seek to incorporate fuel-specific data when available. The original text follows:

“Secondly, we focus on using nighttime light data as a proxy for carbon emissions, which serves as a macro indicator of carbon emissions at the regional level. Additionally, considering the heterogeneity across industries and regions, we incorporate land use data and the GTWR model for further identification. However, due to the lack of more granular carbon emission data, we were unable to further refine the analysis at the industry level. Previous studies have used input-output tables to achieve more detailed sectoral carbon emission estimates; however, due to the large time span of input-output tables, the fit is not ideal[75]. In the future, with updated data, subsequent research can consider improving the carbon emission estimates for different sectors, which will provide more accurate cross-industry and cross-region carbon emission estimates. This will help capture the diversity and complexity of carbon emissions across regions and industries more accurately.”

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Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region

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