Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript provides a comprehensive analysis of the driving factors and decoupling effects of energy-related carbon emissions in Beijing from 2013 to 2020. The study is timely and relevant, given the global focus on achieving carbon neutrality and the significant role of cities in reducing emissions. The authors employ the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) decomposition method, which are appropriate and well-established tools for this type of analysis. The results are insightful and provide valuable evidence for policymakers. However, there are areas where the manuscript can be improved to enhance clarity, methodological rigor, and overall impact.

1.The introduction outlines the importance of studying carbon emissions in Beijing but could more explicitly state the specific research objectives and research questions. For example, what are the primary drivers of carbon emissions reduction, and how does Beijing’s experience inform other cities? This would help readers better understand the study’s focus and contribution.

2.The Tapio decoupling model and LMDI decomposition method are described, but some critical details are missing. For instance, how were the elasticity values calculated for the Tapio model, and what were the specific assumptions or limitations of the LMDI method?
The paper mentions that natural gas was excluded from the analysis due to incomplete data (Section 2.2). This exclusion could significantly impact the results, as natural gas is a transitional fuel. The authors should justify this decision and discuss its potential implications.

3.The results are well-presented, but the discussion could be more critical. For example, the authors attribute the decoupling effect to energy structure optimization and policy implementation but do not explore potential confounding factors, such as the impact of COVID-19 in 2020. A deeper analysis of this anomaly would strengthen the discussion.
The comparison with other studies is useful, but the authors should explicitly highlight how their findings differ or confirm previous research, particularly in the context of Beijing.

4.The policy recommendations (Section 4.3) are relevant but somewhat generic. The authors could provide more specific, actionable suggestions tailored to Beijing’s unique economic and industrial structure. For example, how can Beijing further reduce its reliance on oil in the transportation sector, and what role can green technologies play in achieving this?

Comments on the Quality of English Language

The language requires further refinement.

Author Response

The manuscript provides a comprehensive analysis of the driving factors and decoupling effects of energy-related carbon emissions in Beijing from 2013 to 2020. The study is timely and relevant, given the global focus on achieving carbon neutrality and the significant role of cities in reducing emissions. The authors employ the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) decomposition method, which are appropriate and well-established tools for this type of analysis. The results are insightful and provide valuable evidence for policymakers. However, there are areas where the manuscript can be improved to enhance clarity, methodological rigor, and overall impact.

1. The introduction outlines the importance of studying carbon emissions in Beijing but could more explicitly state the specific research objectives and research questions. For example, what are the primary drivers of carbon emissions reduction, and how does Beijing’s experience inform other cities? This would help readers better understand the study’s focus and contribution.

Response：

Thank you very much for your suggestions on the improvement of the Introduction. We have clarified research objectives and questions and highlighted research contributions in the last paragraph of the Introduction (Line 95-114):

Against this backdrop, the main focus of this paper is on dissecting the drivers of carbon emissions and evaluating the decoupling effects of energy consumption in Beijing between 2013 and 2020 under the dual carbon framework. Specifically, this paper addressed 3 key questions. Firstly, what are the primary drivers of Beijing’s carbon emissions? By calculating carbon emissions associated with energy consumption—classified by industry and energy type—we aimed to identify which sectors and energy sources contribute most significantly to carbon emissions. Secondly, how do carbon emissions and economic growth decouple at different stages of Beijing’s development? Employing the Tapio decoupling model, we analyzed the changes in the relationship between economic momentum and carbon emissions and assess whether Beijing’s policies have successfully mitigated emissions intensity while maintaining economic performance. The comprehensive investigation in this paper adds empirical evidence on how urban energy-related policies and interventions influence carbon emissions in a highly industrialized and populous city.  By integrating Tapio decoupling analysis and a detailed factor decomposition of industries and energy types, we provide nuanced insights that can inform urban policy-making under the dual carbon targets.  Ultimately, this study aims to offer guidance both for Beijing’s continued pursuit of carbon neutrality and for other cities striving to enhance their low-carbon transition strategies, thereby contributing to a broader dialogue on global sustainable development.

 

Besides, we have also added potential primary drivers of carbon emissions reduction in the Research review part from (Line 47-53)

In recent years, considerable research has examined the various influencing factors of carbon emission intensity in energy consumption, yielding an extensive body of literature on how these factors affect carbon emissions. Population factors and urbanization [6], energy structure [7], energy efficiency [8], and economic development [9,10]are frequently regarded as primary contributors to rising carbon emissions. Moreover, these impact factors may vary with the scope of the research (e.g., different regions, time frames, or sectors) [11].

 

 

 

 

2. The Tapio decoupling model and LMDI decomposition method are described, but some critical details are missing. For instance, how were the elasticity values calculated for the Tapio model, and what were the specific assumptions or limitations of the LMDI method?
   The paper mentions that natural gas was excluded from the analysis due to incomplete data (Section 2.2). This exclusion could significantly impact the results, as natural gas is a transitional fuel. The authors should justify this decision and discuss its potential implications.

Response：

Thanks very much for your valuable comments on the methodological details and data range. Regarding the calculation of the elastic coefficient in the Tapio decoupling model and the details of its formula, we will supplement the corresponding formula derivation process, calculation steps, and clarify the applicable conditions and limitations of the model in the revised draft. For the LMDI decomposition method, this study will further explain its basic assumptions, main steps, and possible data limitations in the decomposition of different industries and energy types.

Equation of the Tapio model (Line 163-169):

This article analyzes the decoupling elasticity between economic growth and carbon emissions using the Tapio decoupling model, with calculations shown in Equation (2).

(2)

In the formula,  reflects the decoupling flexibility, indicates the total change in carbon emissions, and illustrates the total change in GDP. According to Table 2, in order to more clearly explain the eight decoupling state classifications of the Tapio model, this study compared and summarized their elastic values.

 

Meanwhile, we have added 4.4. Restrictions and Limitations in Section 4 (Line 472-487).

4.4. Restrictions and Limitations

(1) Fixed Emission Factors. This study assumes that the carbon emission factors of various energy types (such as coal, petroleum, etc.) are constant, and does not consider the micro-differences in fuel quality and combustion efficiency in different years or regions. This assumption may lead to a certain bias in the assessment of the maximum emission reduction potential.

(2) No Residual/Interaction Terms. Since the LMDI decomposition in the form of addition is adopted in this study, no "residual term" will be generated, but some interaction effects (such as the combined impact of industrial structure change and energy efficiency improvement) may also be separated together with the main effect, resulting in a slightly insufficient description of the complex change mechanism.

(3) In lack of Static Interval Comparison. In this study, the overall changes of the base period and the target period are decomposed. Some carbon emission fluctuations caused by temporary policies or short-term economic fluctuations may not be fully reflected in the additive decomposition results, and more detailed dynamic investigation should be carried out in combination with time series analysis or other models.

 

In addition, thanks for your concern about "whether natural gas is excluded". We have reviewed the original text in a revised version and identified ambiguities in the original formulation that may have led readers to misunderstand that natural gas was completely excluded from the study. In fact, we have not ignored the importance of natural gas in carbon emission analysis, including the relevant data and analysis involving natural gas in Table 1 and Figure 2. To avoid any misunderstanding, we have corrected the inaccurate description of "excluding natural gas" in the original text (Line 114-160).

In this context, C reflects the overall carbon emissions from different forms of energy use, measured in ten thousand tons.  denotes the consumption of the energy type of , expressed in terms of standard coal equivalent, also measured in ten thousand tons.  is the carbon emission factor for the energy type of . The variable n refers to the types of fossil fuels, specifically coal, oil and natural gas, thus n equals 3.

The result calculated using the above formula represents the carbon emissions. To convert this result into CO2 emissions, it must be multiplied by the factor 12/44​, which represents the mass fraction of carbon in CO2.

The energy consumption data, denoted by , are sourced from the 'Beijing Energy Statistical Yearbook' for the years 2013 to 2020. Coal and oil consumption is measured in ten thousand tons and must be converted to standard coal equivalent (SCE) units. The conversion factors are derived from the appendix of the 'China Energy Statistical Yearbook 2011', with raw coal having a conversion factor of 0.7143 kgce/kg and crude oil having a factor of 1.4286 kgce/kg. The carbon emission factors for various selected energies are shown in Table 1. GDP and industrial value-added data are obtained from the 'Beijing Statistical Yearbook' for the years 2013 to 2020 and have been adjusted to constant 2013 prices.

Table 1. Carbon emission coefficient of various energy sources.

Coal	Oil	Natural Gas
0.748	0.583	0.444

Figure 2. Carbon emissions related to energy in Beijing from 2013 to 2020 by energy type.

 

3. The results are well-presented, but the discussion could be more critical. For example, the authors attribute the decoupling effect to energy structure optimization and policy implementation but do not explore potential confounding factors, such as the impact of COVID-19 in 2020. A deeper analysis of this anomaly would strengthen the discussion.
   The comparison with other studies is useful, but the authors should explicitly highlight how their findings differ or confirm previous research, particularly in the context of Beijing.

 

 

 

Response：

Thanks for your suggestions. In response to the points you raised, we have conducted a more in-depth analysis of the possibility that anomalous years were affected by the special impact of COVID-19. We have also clarified comparisons with existing research more explicitly and further elaborated on this study’s uniqueness or consistency within the Beijing context. The specific revised passage is as follows (Line 368-435).

4.1. Influencing Factors of Energy-Related Carbon Emissions

After decomposing the model, we find that energy consumption intensity effect and carbon emission factor effect are the main contributors to the reduction of carbon emissions in Beijing over the years. This aligns with the findings of Mi et al., who content reducing energy intensity need not negatively impact economic growth [20]. In Beijing’s case, on the one hand, rationally adjusting the industrial structure can reduce energy intensity while still supporting economic progress. On the other hand, rapid economic development may in turn drive improvements in energy efficiency.

Similar to previous studies, here we observe that GDP development used to be the primary driver of Beijing’s carbon emissions (particularly before 2013), though its relative influence has declined since 2013. This result is consistent with Li et al. [5], who also identify reductions in energy consumption intensity as the most significant factor behind lower emissions in Beijing. However, our findings further highlight that a significant decline in carbon emissions occurred in 2020, which may reflect not only the continued adjustment toward low-carbon industries but also the influence of the COVID-19 pandemic. The economic slowdown and corresponding restrictions in 2020 could have temporarily suppressed energy demand and emissions. Besides, from 2013 to 2020, Beijing has implemented a series of carbon emission control policies such as the Action Plan for Air Pollution Prevention and Control and the Three-year Action Plan for Blue Sky Defense. These policies have significantly reduced carbon emissions by enhancing the industrial framework and boosting energy efficiency and facilitating the transition of the energy system.

From 2013 to 2020, Beijing's coal consumption experienced a significant decrease, while the use of natural gas as a transitional fuel saw a notable increase. While this study primarily examines the emission reduction effects of transitioning from coal to natural gas, Beijing must still deepen the development and utilization of renewable energy sources such as solar and wind power in the future. On one hand, distributed photovoltaic and wind power projects can be widely deployed across urban buildings and industrial parks, reducing reliance on external power supplies. On the other hand, supporting infrastructure (including smart grids and energy storage systems) will enhance the integration and dispatch efficiency of clean energy. When implemented in tandem with the coal-to-gas transition, these measures will not only advance fuel decarbonization but also diversify the energy mix and strengthen the resilience of the power system. Collectively, they will provide sustained support for Beijing and its surrounding regions in accelerating progress toward low-carbon energy transition.

4.2. Evaluation of Decoupling Effect Between Economic Development and Carbon Emissions

Our results demonstrate that Beijing achieved a notable decoupling of economic growth from carbon emissions during 2013–2020, especially following stricter environmental policies and adjustments in energy structure. Strict interventions such as reducing energy-intensive industries, promoting clean energy, and limiting vehicle use (2013–2017), complemented by increasingly efficient energy-saving technologies (post-2018), contributed to this decoupling[21]. Simultaneously, we observe that Shanghai exhibits weaker decoupling characteristics between carbon emissions and economic development, notably due to the substantial proportion of manufacturing and port logistics in its economic structure, resulting in a stronger correlation between its carbon emissions and economic growth. In contrast, Beijing demonstrates relatively stronger decoupling performance in this regard, driven by industrial restructuring and technological innovation[22].

Nevertheless, the year 2020 stands out due to the COVID-19 pandemic, which could have accentuated the reduction in emissions by curtailing certain economic activities. Widespread shutdowns and travel restrictions during the epidemic have "unexpectedly" reduced energy consumption and transportation demand to a certain extent in the short term, thus curbing current carbon emissions. Although these findings confirm earlier conclusions on Beijing’s policy effectiveness [22, 23], they also highlight the importance of considering extraordinary events when evaluating long-term trends. Compared with other regions analyzed in prior studies, Beijing’s context features a relatively high proportion of service-industry output, advanced emission control measures, and rapid technological updates, thereby reinforcing or extending those studies’ insights under a distinctly urban and policy-intensive setting[24, 25].

Looking ahead, the long-term sustainability of Beijing’s decoupling process may face structural constraints. Continued investment in technology renewal and deeper industrial restructuring will be crucial to avoiding growth bottlenecks [26]. Although efforts toward industrial upgrading persist, traditional energy-intensive industries still occupy a non-negligible share of the economy [27]. Consequently, future decoupling strategies in Beijing should balance ongoing industrial transitions with sustained policy efforts, while also accounting for exogenous shocks like pandemics or macroeconomic fluctuations that may further affect emissions trajectories.

4. The policy recommendations (Section 4.3) are relevant but somewhat generic. The authors could provide more specific, actionable suggestions tailored to Beijing’s unique economic and industrial structure. For example, how can Beijing further reduce its reliance on oil in the transportation sector, and what role can green technologies play in achieving this?

Response：

We are grateful for your suggestions on Section 4.3. Future Policy Recommendations. In order to make the policy more relevant to the characteristics of Beijing's economy and industry, we have addressed the relevant issues in the revision, and the specific amendments are as follows (Line 436-471):

4.3. Future Policy Recommendations

The industry-type energy-based accounting identifies key industries (e.g., agriculture, forestry, manufacturing, construction, commerce, information technology) where final demand leads to significant upstream CO₂ emissions. Building on these insights, the Beijing municipal government should establish robust mechanisms to encourage low-carbon consumption and improve transparency. For instance, carbon footprint labeling and tiered carbon taxes can help foster a culture of low-carbon consumption, especially within service sectors and high-traffic commercial areas. Meanwhile, major companies operating in these key industries should publicly disclose CO₂ emissions data across their manufacturing and upstream logistics processes, providing a foundation for more targeted mitigation strategies.

Secondly, transportation sector reliance on fossil fuels—particularly oil—requires more specific interventions. Besides expanding the metro and bus network, Beijing could enhance incentives for purchasing electric and hydrogen fuel cell vehicles, such as offering tax rebates, license plate quotas, or dedicated parking/charging zones. Additionally, building a comprehensive charging and hydrogen refueling infrastructure network would help alleviate concerns about vehicle range and reliability. These measures, coupled with stricter vehicle emissions standards, can significantly reduce transport-related oil consumption.

Thirdly, Beijing needs even stricter ecological and energy-saving regulations to propel additional industrial restructuring and technological innovation [27]. Setting multi-tier carbon standards tailored to specific industries can further encourage cleaner production technologies, particularly in areas like heavy manufacturing [28]. For example, implementing performance-based subsidies or penalties can incentivize enterprises to adopt advanced energy-saving equipment and cleaner production processes.

Furthermore, targeted funds should continue to support high-tech R&D in fields such as digitalization, new energy, and energy-storage technologies, accompanied by discount loans or venture capital programs that spur innovation. "Green Transformation" initiatives can focus on helping traditional high-energy-consuming companies upgrade existing technologies or integrate emerging ones—like AI-based energy management or carbon capture—and thus enable a smoother path toward reducing energy demand and emissions. By tying these recommendations closely to Beijing’s unique industrial structure and expanding service economy, policymakers can ensure that high-growth sectors transition swiftly to cleaner energy sources, while also reinforcing the city’s leading role in climate innovation.

 

 

 

 

 

5. The language requires further refinement.

Response：

We appreciate your feedback on the language. We have refined and adjusted the English academic expression of the full text, and made efforts to improve the professionalism and coherence of the text from the aspects of wording and logical cohesion, so as to make the overall discussion more smooth and in line with academic norms.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Report on the Manuscript ID: sustainability-3537257
"Driving factors and decoupling effect of energy-related carbon emissions in Beijing, 2013-2020"

    The study analyzes energy-related carbon emissions in Beijing from 2013 to 2020. Using the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method, the authors assess the decoupling effect between economic growth and carbon emissions. The results indicate that Beijing achieved absolute decoupling through policy implementation, structural energy consumption changes, and technological advancements. The paper offers insights into sustainable development strategies and low-carbon economic models that could benefit other cities and regions. Overall, the paper presents a well-structured and insightful analysis of carbon emissions and decoupling in Beijing. It contributes valuable empirical evidence to the field of sustainability and energy policy. Addressing the identified areas for improvement would further enhance its impact and applicability to policymakers and researchers working on low-carbon transitions as follows:

1. How does Beijing's performance in decoupling carbon emissions compare to other major cities in China or globally? Would a comparative analysis strengthen the policy implications of the study?
2. What are the potential limitations of the study? How do uncertainties in emissions calculations or external factors like the COVID-19 pandemic impact the results?
3. What strategies could support China's carbon neutrality goals for 2060?
4. While the paper discusses the transition from coal to natural gas, what role do renewable energy sources like solar and wind play in Beijing's energy transition? Could a deeper discussion on this aspect provide additional value?

Author Response

The study analyzes energy-related carbon emissions in Beijing from 2013 to 2020. Using the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method, the authors assess the decoupling effect between economic growth and carbon emissions. The results indicate that Beijing achieved absolute decoupling through policy implementation, structural energy consumption changes, and technological advancements. The paper offers insights into sustainable development strategies and low-carbon economic models that could benefit other cities and regions. Overall, the paper presents a well-structured and insightful analysis of carbon emissions and decoupling in Beijing. It contributes valuable empirical evidence to the field of sustainability and energy policy. Addressing the identified areas for improvement would further enhance its impact and applicability to policymakers and researchers working on low-carbon transitions as follows:

1.How does Beijing's performance in decoupling carbon emissions compare to other major cities in China or globally? Would a comparative analysis strengthen the policy implications of the study?

Response：

We would like to thank you for your suggestions on cross-cutting comparisons. In order to further highlight the policy implications of this study, we will combine the carbon emission reduction characteristics of major domestic cities, and compare and analyze the decoupling effect of Beijing in industrial structure upgrading, technology investment and policy implementation. Specific relevant sections are added in Section 4.2(Line 387-409).

4.2. Evaluation of Decoupling Effect Between Economic Development and Carbon Emissions

Our results demonstrate that Beijing achieved a notable decoupling of economic growth from carbon emissions during 2013–2020, especially following stricter environmental policies and adjustments in energy structure. Strict interventions such as reducing energy-intensive industries, promoting clean energy, and limiting vehicle use (2013–2017), complemented by increasingly efficient energy-saving technologies (post-2018), contributed to this decoupling. Simultaneously, we observe that Shanghai exhibits weaker decoupling characteristics between carbon emissions and economic development, notably due to the substantial proportion of manufacturing and port logistics in its economic structure, resulting in a stronger correlation between its carbon emissions and economic growth. In contrast, Beijing demonstrates relatively stronger decoupling performance in this regard, driven by industrial restructuring and technological innovation.[1]

Nevertheless, the year 2020 stands out due to the COVID-19 pandemic, which could have accentuated the reduction in emissions by curtailing certain economic activities. Widespread shutdowns and travel restrictions during the epidemic have "unexpectedly" reduced energy consumption and transportation demand to a certain extent in the short term, thus curbing current carbon emissions. Although these findings confirm earlier conclusions on Beijing’s policy effectiveness [21,22], they also highlight the importance of considering extraordinary events when evaluating long-term trends. Compared with other regions analyzed in prior studies, Beijing’s context features a relatively high proportion of service-industry output, advanced emission control measures, and rapid technological updates, thereby reinforcing or extending those studies’ insights under a distinctly urban and policy-intensive setting.

2.What are the potential limitations of the study? How do uncertainties in emissions calculations or external factors like the COVID-19 pandemic impact the results?

Response：

We are very grateful for your valuable suggestions. We discuss potential limitations of the study more fully in the section 4.4 Restrictions and Limitations of the revisions, including how uncertainties in carbon emissions data may affect the results (Line 453-468).

4.4. Restrictions and Limitations

(1) Fixed Emission Factors. This study assumes that the carbon emission factors of various energy types (such as coal, petroleum, etc.) are constant, and does not consider the micro-differences in fuel quality and combustion efficiency in different years or regions. This assumption may lead to a certain bias in the assessment of the maximum emission reduction potential.

(2) No Residual/Interaction Terms. Since the LMDI decomposition in the form of addition is adopted in this study, no "residual term" will be generated, but some interaction effects (such as the combined impact of industrial structure change and energy efficiency improvement) may also be separated together with the main effect, resulting in a slightly insufficient description of the complex change mechanism.

(3) In lack of Static Interval Comparison. In this study, the overall changes of the base period and the target period are decomposed. Some carbon emission fluctuations caused by temporary policies or short-term economic fluctuations may not be fully reflected in the additive decomposition results, and more detailed dynamic investigation should be carried out in combination with time series analysis or other models.

 

At the same time, the temporary impact of external factors such as COVID-19 on economic activity and emission intensity will also be included in the discussion to help readers more objectively understand the applicability and stability of the research conclusions.

Line 340 - Line 348

Similar to previous studies, here we observe that GDP development used to be the primary driver of Beijing’s carbon emissions—particularly before 2013—though its relative influence has declined since 2013. This result is consistent with Li et al. [5], who also identify reductions in energy consumption intensity as the most significant factor behind lower emissions in Beijing. However, our findings further highlight that a significant decline in carbon emissions occurred in 2020, which may reflect not only the continued adjustment toward low-carbon industries but also the influence of the COVID-19 pandemic. The economic slowdown and corresponding restrictions in 2020 could have temporarily suppressed energy demand and emissions. Besides, from 2013 to 2020, Beijing has implemented a series of carbon emission control policies such as the Action Plan for Air Pollution Prevention and Control and the Three-year Action Plan for Blue Sky Defense. These policies have significantly reduced carbon emissions by enhancing the industrial framework and boosting energy efficiency and facilitating the transition of the energy system.

And Line 367 to Line 377

Nevertheless, the year 2020 stands out due to the COVID-19 pandemic, which could have accentuated the reduction in emissions by curtailing certain economic activities. Widespread shutdowns and travel restrictions during the epidemic have "unexpectedly" reduced energy consumption and transportation demand to a certain extent in the short term, thus curbing current carbon emissions. Although these findings confirm earlier conclusions on Beijing’s policy effectiveness [22, 23], they also highlight the importance of considering extraordinary events when evaluating long-term trends. Compared with other regions analyzed in prior studies, Beijing’s context features a relatively high proportion of service-industry output, advanced emission control measures, and rapid technological updates, thereby reinforcing or extending those studies’ insights under a distinctly urban and policy-intensive setting[24, 25].

 

3. What strategies could support China's carbon neutrality goals for 2060?

Response：

Thank you for your questions. In order to further echo China's 2060 carbon neutrality goal, it is proposed to focus on the following points based on the existing policy path, and the relevant paragraphs have been added to Section 4.4:1. Strengthen market-based measures such as carbon footprint labeling and differentiated carbon taxes in key industries (such as agriculture, manufacturing, construction, etc.) to promote coordinated emission reduction between the consumer side and the supply chain; 2. Accelerate the transformation of the transportation structure and encourage the popularization of electric and hydrogen vehicles through financial incentives and infrastructure improvement; 3. Formulate more stringent ecological and energy-saving regulations, implement multi-level carbon standards for different industries, and cooperate with high-tech research and development and financial support. These initiatives will further enhance Beijing's innovative leadership role in deepening the low-carbon transition and contributing to carbon neutrality by 2060.

(Line 418-452)

4.3. Future Policy Recommendations

The industry-type energy-based accounting identifies key industries (e.g., agriculture, forestry, manufacturing, construction, commerce, information technology) where final demand leads to significant upstream CO₂ emissions. Building on these insights, the Beijing municipal government should establish robust mechanisms to encourage low-carbon consumption and improve transparency. For instance, carbon footprint labeling and tiered carbon taxes can help foster a culture of low-carbon consumption, especially within service sectors and high-traffic commercial areas. Meanwhile, major companies operating in these key industries should publicly disclose CO₂ emissions data across their manufacturing and upstream logistics processes, providing a foundation for more targeted mitigation strategies.

Secondly, transportation sector reliance on fossil fuels—particularly oil—requires more specific interventions. Besides expanding the metro and bus network, Beijing could enhance incentives for purchasing electric and hydrogen fuel cell vehicles, such as offering tax rebates, license plate quotas, or dedicated parking/charging zones. Additionally, building a comprehensive charging and hydrogen refueling infrastructure network would help alleviate concerns about vehicle range and reliability. These measures, coupled with stricter vehicle emissions standards, can significantly reduce transport-related oil consumption.

Thirdly, Beijing needs even stricter ecological and energy-saving regulations to propel additional industrial restructuring and technological innovation [28]. Setting multi-tier carbon standards tailored to specific industries can further encourage cleaner production technologies, particularly in areas like heavy manufacturing [29]. For example, implementing performance-based subsidies or penalties can incentivize enterprises to adopt advanced energy-saving equipment and cleaner production processes.

Furthermore, targeted funds should continue to support high-tech R&D in fields such as digitalization, new energy, and energy-storage technologies, accompanied by discount loans or venture capital programs that spur innovation. "Green Transformation" initiatives can focus on helping traditional high-energy-consuming companies upgrade existing technologies or integrate emerging ones—like AI-based energy management or carbon capture—and thus enable a smoother path toward reducing energy demand and emissions. By tying these recommendations closely to Beijing’s unique industrial structure and expanding service economy, policymakers can ensure that high-growth sectors transition swiftly to cleaner energy sources, while also reinforcing the city’s leading role in climate innovation[30].

 

 

 

4. While the paper discusses the transition from coal to natural gas, what role do renewable energy sources like solar and wind play in Beijing's energy transition? Could a deeper discussion on this aspect provide additional value?

Response：

Thank you for following our contributions on the topic of renewable energy. Beijing's transition from coal to gas is also accelerating the deployment of clean power supplies such as photovoltaic and wind power. In the future, if we can strengthen the technology research and development and large-scale deployment of distributed renewable energy such as solar energy and wind energy, and continue to invest in supporting infrastructure such as energy storage and smart grid, it will further consolidate the effect of Beijing's low-carbon transformation. We have added relevant statements in the discussion of Section 4.1, as follows:

Line 391-403

4.1. Influencing Factors of Energy-Related Carbon Emissions

From 2013 to 2020, Beijing's coal consumption experienced a significant decrease, while the use of natural gas as a transitional fuel saw a notable increase. While this study primarily examines the emission reduction effects of transitioning from coal to natural gas, Beijing must still deepen the development and utilization of renewable energy sources such as solar and wind power in the future. On one hand, distributed photovoltaic and wind power projects can be widely deployed across urban buildings and industrial parks, reducing reliance on external power supplies. On the other hand, supporting infrastructure (including smart grids and energy storage systems) will enhance the integration and dispatch efficiency of clean energy. When implemented in tandem with the coal-to-gas transition, these measures will not only advance fuel decarbonization but also diversify the energy mix and strengthen the resilience of the power system. Collectively, they will provide sustained support for Beijing and its surrounding regions in accelerating progress toward low-carbon energy transition.

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. Clarify the Novel Contribution: The study applies standard models to a well-researched topic. The originality and added value compared to existing literature are not clearly highlighted. Please clarify what new insights this paper brings beyond prior work on Beijing’s carbon emissions and LMDI-based decomposition.
2. Improve Methodological Transparency: The application of the LMDI model and sector-based emission calculations lacks clarity. Specifically: * The treatment of natural gas is inconsistent—excluded from the model equations but discussed in results. * The assumptions and data sources behind sectoral energy distribution should be better explained. * Clarify unit conversions and standardization procedures. 
3. Strengthen Result Interpretation: The findings are mostly descriptive. A more analytical interpretation—such as inter-industry comparison or linking observed changes with specific policy interventions—would significantly enhance the value of the study.
4. Enhance Figure/Table Integration: Figures and tables are useful but are not well-cited or sufficiently discussed in the text. Ensure figures/tables are cited in proper sequence and that key findings from them are thoroughly analyzed.
5. Refine Language and Structure: The manuscript contains minor grammatical errors and awkward sentence structures that should be corrected to enhance readability and precision. Consider a professional English editing service.

Comments on the Quality of English Language

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

Author Response

1.Clarify the Novel Contribution: The study applies standard models to a well-researched topic. The originality and added value compared to existing literature are not clearly highlighted. Please clarify what new insights this paper brings beyond prior work on Beijing’s carbon emissions and LMDI-based decomposition.

Response：

Thank you for your suggestions on the clarification of the novel contribution. While prior studies on Beijing’s emissions often use decomposition methods (e.g., LMDI) or assess the relationship between emissions and economic growth in isolation, this paper integrates both a Tapio decoupling analysis and a detailed factor decomposition (by industry and energy type) within the dual carbon policy context. By capturing the 2013–2020 timeframe—when Beijing underwent a marked shift from coal to natural gas—we not only identify the primary drivers of emissions but also assess how effectively Beijing’s economic and policy measures have decoupled growth from carbon outputs during this transformative period. Specifically, we have elaborated the novel contribution in the Introduction section (Line 95-114).

Against this backdrop, the main focus of this paper is on dissecting the drivers of carbon emissions and evaluating the decoupling effects of energy consumption in Beijing between 2013 and 2020 under the dual carbon framework. Specifically, this paper addressed 3 key questions. Firstly, what are the primary drivers of Beijing’s carbon emissions? By calculating carbon emissions associated with energy consumption—classified by industry and energy type—we aimed to identify which sectors and energy sources contribute most significantly to carbon emissions. Secondly, how do carbon emissions and economic growth decouple at different stages of Beijing’s development? Employing the Tapio decoupling model, we analyzed the changes in the relationship between economic momentum and carbon emissions and assess whether Beijing’s policies have successfully mitigated emissions intensity while maintaining economic performance. The comprehensive investigation in this paper adds empirical evidence on how urban energy-related policies and interventions influence carbon emissions in a highly industrialized and populous city. By integrating Tapio decoupling analysis and a detailed factor decomposition of industries and energy types, we provide nuanced insights that can inform urban policy-making under the dual carbon targets.  Ultimately, this study aims to offer guidance both for Beijing’s continued pursuit of carbon neutrality and for other cities striving to enhance their low-carbon transition strategies, thereby contributing to a broader dialogue on global sustainable development.

 

 

2.Improve Methodological Transparency: The application of the LMDI model and sector-based emission calculations lacks clarity. Specifically: * The treatment of natural gas is inconsistent—excluded from the model equations but discussed in results. * The assumptions and data sources behind sectoral energy distribution should be better explained. * Clarify unit conversions and standardization procedures. 

Response：

Thanks for your suggestions on Methodological Details and Transparency. Firstly, we have reviewed the original text in a revised version and identified ambiguities in the original formulation that may have led readers to misunderstand that natural gas was completely excluded from the study. In fact, we have not ignored the importance of natural gas in carbon emission analysis, including the relevant data and analysis involving natural gas in Table 1 and Figure 2. To avoid any misunderstanding, we have corrected the inaccurate description of "excluding natural gas" in the original text (Line 144-160).

In this context, C reflects the overall carbon emissions from different forms of energy use, measured in ten thousand tons.  denotes the consumption of the energy type of , expressed in terms of standard coal equivalent, also measured in ten thousand tons.  is the carbon emission factor for the energy type of . The variable n refers to the types of fossil fuels, specifically coal, oil and natural gas, thus n equals 3.

The result calculated using the above formula represents the carbon emissions. To convert this result into CO2 emissions, it must be multiplied by the factor 12/44​, which represents the mass fraction of carbon in CO2.

The energy consumption data, denoted by , are sourced from the 'Beijing Energy Statistical Yearbook' for the years 2013 to 2020. Coal and oil consumption is measured in ten thousand tons and must be converted to standard coal equivalent (SCE) units. The conversion factors are derived from the appendix of the 'China Energy Statistical Yearbook 2011', with raw coal having a conversion factor of 0.7143 kgce/kg and crude oil having a factor of 1.4286 kgce/kg. The carbon emission factors for various selected energies are shown in Table 1. GDP and industrial value-added data are obtained from the 'Beijing Statistical Yearbook' for the years 2013 to 2020 and have been adjusted to constant 2013 prices.

(Line 161)

Table 1. Carbon emission coefficient of various energy sources.

Coal	Oil	Natural Gas
0.748	0.583	0.444

(Line 281)

Figure 2. Carbon emissions related to energy in Beijing from 2013 to 2020 by energy type.

Besides, we explained in detail the assumptions and data sources behind the sector's energy distribution in Section 2.1 Data Acquisition.  (Line 115-134)

2.1. Data Acquisition and Processing

We extracted annual fuel consumption (coal, oil, and natural gas) by sector directly from the Beijing Statistical Yearbook, which disaggregates industries according to the official classification used by the National Bureau of Statistics (NBS). We assume that the sectoral distribution of energy consumption follows the categories reported in the Beijing Statistical Yearbook without further re-classification, thus aligning our results with official definitions of industrial and service sub-sectors.

Original energy data are typically reported in physical units (e.g., metric tons of coal, cubic meters of natural gas). To facilitate comparison and aggregation, we converted these physical units into tons of standard coal equivalents (TCE) following the conversion factors published in the China Energy Statistical Yearbook. For calculating carbon emissions, we applied the officially recommended conversion factors for each fuel type (coal, oil, and natural gas), which reflect differences in average calorific values and carbon contents. Where necessary, we supplemented these factors with IPCC guidelines (2006 IPCC Guidelines for National Greenhouse Gas Inventories) to maintain compatibility with international standards.

GDP data were in current prices as reported yearly. Any inter-year analyses requiring constant prices used the GDP deflator from the Beijing Statistical Yearbook to adjust for inflation.

 

3. Strengthen Result Interpretation: The findings are mostly descriptive. A more analytical interpretation—such as inter-industry comparison or linking observed changes with specific policy interventions—would significantly enhance the value of the study.

Response：

Thank you for highlighting the need for a more analytical interpretation of our findings. We appreciate your suggestion to include inter-industry comparisons and clearly link the observed changes to specific policy interventions. In our revision, we strengthened our analysis by expanding inter-industry comparisons and enhance policy linkages

(Line 213-280).

3. Results

3.1. Energy-Related Carbon Emissions Assessment

As shown in Fig.1, based on the data provided for Beijing's energy-related carbon emissions from 2013 to 2020, energy-related carbon emissions were segmented by primary, secondary, and tertiary industries. Overall, a notable downward trend is observed across all three sectors—a strong indication that various policy measures, technological advancements, and structural economic shifts have collectively fostered reduced carbon intensity.

The carbon emissions from the primary industry have shown a consistent decline over the years. Starting at 43.09 tc in 2013, emissions decreased to 31.81 tc by 2020. While this sector already represents a relatively small share of Beijing’s GDP, the reduction may be linked to policy-driven improvements in agricultural efficiency, stricter environmental guidelines for rural activities, and scaled-back primary industry operations as Beijing’s economy continues to urbanize and service-oriented industries expand. The secondary industry, which typically includes manufacturing and construction, dropped significantly from 920.74 tc in 2013 to 693.21 tc in 2020. This substantial reduction could be attributed to the Beijing Clean Air Action Plan (initiated in 2013), which included replacing coal-fired industrial boilers with cleaner fuels, setting tighter emissions standards, and encouraging the relocation or upgrading of high-emission manufacturing plants. Additionally, improved industrial energy efficiency—driven by technological innovations and economic incentives—appears to have accelerated the sector’s decarbonization. Emissions from the tertiary industry, encompassing services and other non-industrial sectors, fell from 1376.82 tc in 2013 to 1122.69 tc in 2020. This decrease reflects Beijing’s continuous shift toward a modern service economy that relies more on digitalized operations and energy-efficient buildings. Policy interventions—such as green building codes, widespread adoption of electrified public transport, and incentives to promote low-carbon office environments—likely contributed to this downward trend.

Taken together, the observed inter-industry reductions underscore Beijing’s evolving economic structure and the impact of targeted environmental policies. Although emissions remain largest in the tertiary sector due to the sheer scale of service activities, the overall downward trajectory across all industries highlights the city’s notable progress in mitigating carbon emissions. Continued policy support aimed at efficiency improvements and cleaner energy sources will be essential to maintaining and accelerating these gains.

Figure 2 analyzes Beijing’s energy-related carbon emissions based on coal, oil, and natural gas consumption from 2013 to 2020, revealing distinct patterns tied to specific policy initiatives and market shifts.

Coal emissions dropped markedly from 921.59 tc in 2013 to just 59.80 tc in 2020. This steep decline aligns with Beijing’s targeted coal abatement policies, including the closure of coal-fired power plants within the municipality, the conversion of residential heating systems to natural gas or electricity, and the enforcement of stringent emissions standards. These measures were part of an aggressive strategy to enhance air quality and public health. Oil emissions showed moderate fluctuation, beginning at 991.94 tc in 2013 and peaking at 1072.92 tc in 2019 before decreasing to 908.97 tc in 2020. This trend reflects the complexity of phasing out oil, which is heavily used in transportation and certain industrial applications. While policy-driven improvements—such as upgraded vehicle emissions standards, expanded electric public transport systems, and subsidies for electric vehicles—likely tempered oil consumption, ongoing economic activities and travel demands keep oil a significant source of emissions. The slight dip in 2020 may partly stem from these cleaner transport policies and a gradual increase in alternative-fuel adoption. Emissions from natural gas climbed consistently from 427.12 tc in 2013 to 878.95 tc in 2020. Although natural gas still produces carbon emissions, its rise in Beijing’s energy mix signifies a broader shift to cleaner-burning fuels. As part of a transitional strategy, natural gas replaced a sizeable portion of coal usage, mitigating both carbon and local air pollutant emissions. This transition was further supported by expanded pipeline networks and government investments in gas-fired heating and power generation.

From a broader perspective, the rapid reduction in coal emissions and the increasing reliance on natural gas underscores a deliberate transition towards lower-carbon fuels. Oil remains a challenge area, indicating opportunities for further policy action—particularly in transport electrification, industrial process innovations, and increased use of biofuels or hydrogen. Overall, Beijing’s energy mix reshaping demonstrates tangible successes in coal reduction strategies and highlights the potential for continued progress through investment in renewable energy, energy efficiency improvements, and sustained technological innovation.

(Line 283-308).

3.2. Analysis of Decoupling Status in Beijing

As illustrated in Figure 3, Beijing has experienced a state of absolute decoupling from 2014 to 2020. During this period, the economy continued to grow while carbon emissions underwent an effective reduction. The decoupling elasticity index remained negative throughout, indicating that emissions declined at a faster rate than GDP grew.

From 2014 to 2017, the index dropped from -0.10 to approximately -0.60, suggesting a deepening degree of decoupling, likely driven by tighter environmental regulations under Beijing’s Clean Air Action Plans (e.g., restricting coal consumption, enforcing stricter emission standards in industry and transport) and ongoing shifts in the city’s industrial structure. Although the index value in 2018 and 2019 increased slightly (meaning the absolute value became smaller), it still stayed below zero, implying continued absolute decoupling despite a modest slowdown in the momentum of emission reductions.

Notably, the index reached -2.81 in 2020, reflecting that the reduction in carbon emissions that year far outpaced GDP growth. While this jump partly underscores the effectiveness of Beijing’s long-term efforts to curb emissions, it was also likely influenced by the COVID-19 pandemic, which suppressed economic activity but further curbed carbon output. Going forward, maintaining absolute decoupling will depend on how well Beijing sustains its policy mechanisms and technological investments once pandemic-related constraints ease. By analyzing the interplay between ongoing initiatives (e.g., phasing out coal-fired boilers, promoting clean vehicles) and broader economic recovery, policymakers can gauge how robustly this decoupled growth pattern can endure in the post-pandemic era.

(Line 309-340).

3.3. Influencing Factors of Energy-Related Carbon Emissions

As shown in Table.3 and Fig.4, the data provided illustrates the Logarithmic Mean Divisia Index (LMDI) decomposition analysis of carbon emissions in Beijing, broken down into various contributing factors from 2013 to 2020.

During the air pollution prevention and control action plan (2013–2017), carbon emissions linked to economic growth (i.e., GDP effect) remained consistently strong (1,894.6–2,072.6 million tons), illustrating that expansion in industrial output and services continued to generate upward pressure on emissions. This trend underscores the challenge of achieving growth while limiting carbon output, emphasizing the need for more robust measures in energy efficiency and cleaner production. The notable negative values of industrial structure effects (-134.1 to -490.0 million tons) highlight significant restructuring of Beijing’s economy away from high energy-intensive sectors. These negative contributions suggest the effectiveness of policy measures aimed at phasing out polluting industries and promoting higher-value, lower-emission sectors, such as technology and services. Energy consumption intensity effect provided the largest downward pull on emissions, ranging from -1,952.0 to -3,279.2 million tons. The marked decline in energy consumption per unit of GDP reflected improvements in industrial processes, adoption of higher-efficiency technologies, and stricter energy auditing and management initiatives. The prominence of this factor suggests that further scaling up efficiency standards can yield even greater emission reductions. The energy structure effect alternated between positive and negative contributions but remained overall negative, at 371.3, 622.7, 1,087.9, and 1,362.0 million tons. This pattern indicates that increasing the share of clean and low-carbon energy (e.g., natural gas, renewables) made notable inroads, albeit with occasional variability. The rising negative contribution points toward a growing reliance on cleaner energy options, a direction that—if accelerated—can solidify long-term decarbonization gains. The carbon emission factor effect consistently showed negative contributions that increased over time, recorded at -371.3, -622.7, -1,087.9, and -1,362.0 million tons, indicating that a reduction in carbon emission factors (such as raising carbon emission standards) significantly lowered carbon emissions.

In the three-year action plan phase for the Blue Sky Defense campaign (2017-2020), carbon emissions continued to decline, with a total reduction of -1,665.4 million tons. The GDP effect decreased markedly from 2039.9 to 347.4 million tons. This could reflect both the effects of the Blue Sky campaign’s stringent measures and broader shifts in economic activity, especially in 2020 when COVID-19 dampened economic expansion. The negative contribution from industrial structure effects weakened, with figures being from -138.4 to -70.7 million tons. This could be due to diminishing returns from earlier restructuring efforts or new economic pressures—such as attracting certain industries or grappling with slower growth—that hindered further large-scale transformation. The energy consumption intensity effect continued to be negative, recorded at -2,257.6, -1,426.5, and -1,293.2 million tons. Additional technology upgrades and deeper structural changes (e.g., deploying smart grids, green building programs) could reignite larger future gains. The energy structure effect underscored tightened emission standards, upgraded pollution controls, and advanced technology adoption that effectively lowered emission factors, moving from -356.1 and -293.8 to -1,015.5 million tons. Notably, the significant negative figure of -1,015.5 million tons in the final year underscores how renewables expansion and a shift from coal to natural gas contributed to meaningful emissions reductions—especially against the backdrop of the Blue Sky Defense campaign. The carbon emission factor effect continued to show negative contributions, significantly increasing in 2020, with figures from -356.1 to -1,015.5 million tons, further underscoring the role of enhanced carbon emission standards and technological advancement in reducing carbon emissions.

 

4. Enhance Figure/Table Integration: Figures and tables are useful but are not well-cited or sufficiently discussed in the text. Ensure figures/tables are cited in proper sequence and that key findings from them are thoroughly analyzed.

Response：

Thank you for pointing out the need for stronger integration of figures and tables in our manuscript. We have made proper sequencing and citation, as well as adding thorough analysis of key findings.

(Line 166-170).

In the formula,  reflects the decoupling flexibility, indicates the total change in carbon emissions, and illustrates the total change in GDP. According to Table 2, in order to more clearly explain the eight decoupling state classifications of the Tapio model, this study compared and summarized their elastic values.

Table 2. Comparison of the 8 Grades of Tapio and their Elasticity Values.

Conditions
Negative Decoupling	Decoupling of growth	Positive	Positive	＞1.2
	Strong negative decoupling	Positive	Negative	＜0
	Weak negative decoupling	Negative	Negative	0≤＜0.8
Decoupling	Weak decoupling	Positive	Positive	0≤＜0.8
	Strong decoupling	Negative	Positive	＜0
	Recessive decoupling	Negative	Negative	＞1.2
Coupling	Growth coupling	Positive	Positive	0≤＜0.8
	Recessive coupling	Negative	Negative	0≤＜0.8

 

(Line 309-340).

As shown in Table.3 and Fig.4, the data provided illustrates the Logarithmic Mean Divisia Index (LMDI) decomposition analysis of carbon emissions in Beijing, broken down into various contributing factors from 2013 to 2020.

Table 3. LMDI decomposition of carbon emissions from production and energy consumption in Beijing from 2013-2020 (unit: million tc).

Conditions	period	GDP effect	Industrial structure effect	Energy consumption intensity effect	Energy structure effect	Carbon emission factor effect	Total
Air Pollution Prevention and Control Action Plan Phase	2013-2014	189.46	-13.41	-195.20	37.13	-37.13	-19.14
	2014-2015	176.78	-49.00	-190.04	62.27	-62.27	-62.27
	2015-2016	192.19	-22.76	-278.21	108,79	-108.79	-108.79
	2016-2017	207.26	-15.53	-327.92	136.20	-136.20	-136.20
Three-Year Action Plan for the Blue Sky Defense Battle	2017-2018	203.99	-13.84	-225.76	35.61	-35.61	-35.61
	2018-2019	133.99	-20.71	-142.65	29.38	-29.38	-29.38
	2019-2020	34.84	-7.07	-129.32	101.55	-101.55	-101.55

 

Figure 4. LMDI decomposition of carbon emissions from production and energy consumption in Beijing from 2013-2020.

 

5.Refine Language and Structure: The manuscript contains minor grammatical errors and awkward sentence structures that should be corrected to enhance readability and precision. Consider a professional English editing service.

Response：

Thank you for highlighting the need to refine the language and structure of our manuscript. We have carefully reviewed the text for any grammatical or stylistic issues and made the necessary revisions to ensure clarity and precision. We have considered using a professional English editing service to further enhance the quality of the writing. We appreciate your feedback and will work diligently to improve the readability of the manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have done a good job of answering most of the concerns raised.

Author Response

Reviewer 2

The authors have done a good job of answering most of the concerns raised.

Response：

We sincerely thank you for your detailed review of our work and valuable feedback. Your suggestions not only helped us further improved the quality of the paper, but also enhanced the depth of our research work.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

A few typographical errors remain (e.g., "oenhancing" in Line 374). Authors may wish to double-check figure resolution and formatting for final production.

Author Response

The italic and underlined paragraphs are comments made by reviewers

The paragraphs with blue colors are unchanged paragraphs in the original text.

The paragraphs with red colors are revised or added paragraphs in the original text.

Reviewer3

A few typographical errors remain (e.g., "oenhancing" in Line 374). Authors may wish to double-check figure resolution and formatting for final production.

Response：

Thank the reviewers for pointing out the typesetting errors. We have carefully examined the entire text and corrected all the errors found, including the word "oenhancing" in line 374.

In addition, we also reviewed the resolution and format of the charts and ensured that they met the requirements for final publication. Thank you to the reviewers for your valuable suggestions. These improvements will help enhance the quality of our paper.

(Line 387)

Similar to previous studies, her we observe that GDP development used to be the primary driver of Beijing’s carbon emissions—particularly before 2013—though its relative influence has declined since 2013. This result is consistent with Li et al. [5], who also identify reductions in energy consumption intensity as the most significant factor behind lower emissions in Beijing. However, our findings further highlight that a significant decline in carbon emissions occurred in 2020, which may reflect not only the continued adjustment toward low-carbon industries but also the influence of the COVID-19 pandemic. The economic slowdown and corresponding restrictions in 2020 could have temporarily suppressed energy demand and emissions. Besides, from 2013 to 2020, Beijing has implemented a series of carbon emission control policies such as the Action Plan for Air Pollution Prevention and Control and the Three-year Action Plan for Blue Sky Defense. These policies have significantly reduced carbon emissions by enhancing the industrial framework and boosting energy efficiency and facilitating the transition of the energy system.

 

(Line 387)

 

Figure 1. Carbon Emissions Related to Energy by Industry Type in Beijing from 2013 to 2020.

 

(Line 283)

 

Figure 2. Carbon emissions related to energy in Beijing from 2013 to 2020 by energy type.

 

(Line 308)

 

Figure 3. Changes in the Energy-Related Carbon Emission Decoupling Elasticity Index in Beijing from 2013 to 2020.

 

(Line 365)

 

Figure 4. LMDI decomposition of carbon emissions from production and energy consumption in Beijing from 2013-2020.

Author Response File: Author Response.pdf