Looking Upward or Downward? The Prioritization of Energy Policy in Local Implementation: County-Level Evidence from China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. In the initial part of the Introduction, the authors diagnose the situation in China based on the literature from 2015 and 2011. The situation is similar in the case of policy implementation, citing information from 2011 and 2012. The article therefore gives the impression that it has already been submitted for publication and rejected. For the implementation of energy policy, especially in China, a ten-year period is a period of rapid and dynamic changes, which must be taken into account in research. I therefore suggest updating the literature review and preparing a research gap and research questions on this basis.
2. In lines 143-145 we have information based on the 2015 item – as I mentioned above: the work should be updated, because for the implementation of energy policy a ten-year period is very long.
3. The details of the study are very general (lines 279-296). It is not clear how many interviews were conducted and with whom, which also applies to field visits. There is no coherent method. It is also unclear how the rankings were determined (343).
4. It is also unclear what period the analysis covers.
5. The analysis process itself is not explained: the authors do not explain why they include other CGs (507) – the analysis process should be explained and described in the abstract.
6. The conclusions (577) do not follow directly from the study and are more on a general nature.

Author Response

1. In the initial part of the Introduction, the authors diagnose the situation in China based on the literature from 2015 and 2011. The situation is similar in the case of policy implementation, citing information from 2011 and 2012. The article therefore gives the impression that it has already been submitted for publication and rejected. For the implementation of energy policy, especially in China, a ten-year period is a period of rapid and dynamic changes, which must be taken into account in research. I therefore suggest updating the literature review and preparing a research gap and research questions on this basis.

Thank you very much for your suggestions. We made the following modifications:

Incorporating the latest developments in China’s low-carbon energy transition, we have updated our references with sources from both domestic and international literature, primarily focusing on the period 2020-2025. Additionally, to maintain theoretical traceability, we have retained some classic literature in the field.

Through a systematic review of recent literature, we have further refined the identified research gaps (see lines 95-119):

Existing studies have provided insights into local governments’ energy policy prioritization behaviour from organizational, individual, and policy dimensions, yet gaps remain for further exploration: (1) It fails to explain why policy prioritization differs among local governments with similar organizational structures. Currently, China’s low-carbon energy transition exhibits weak incentives and constraints—it neither directly contributes to officials’ political advancement nor has clear accountability cases emerged. Moreover, local governments at the same administrative level within a jurisdiction share identical institutional environments for energy transition, making structural perspectives inadequate for explaining its policy logic. (2) It overlooks inter-actor interactions. Policy choices for low-carbon energy transition affect the distribution of interests across various entities, including governments at different levels and enterprises. In other words, local governments’ policy prioritization is not shaped solely by pressure from a single type of stakeholder but is embedded within complex internal and external governmental relations. As interactions among different stakeholders vary, so too does the government’s prioritization of ‘whose interests to protect first’ leading to corresponding shifts in policy sequencing. (3) It cannot clarify why policy sequencing differs despite identical policy tools and attributes. While policy-attribute research reveals general attention allocation patterns, it seldom addresses variations in local attention distribution. An overview of government attention allocation theories reveals that the three levels—organizational, individual, and policy—are not entirely isolated. The allocation of government attention is a subjective choice shaped by objective contexts, and no single level alone provides a complete explanation. Adopting a systemic perspective to examine the interactions among these levels thus emerges as a viable approach to understanding government attention allocation.

Building on this foundation, we have distilled our research questions as follows (lines 120-126):

How do government internal-external relations and policy attributes influence local governments?

 

2. In lines 143-145 we have information based on the 2015 item – as I mentioned above: the work should be updated, because for the implementation of energy policy a ten-year period is very long.

Thank you very much for your suggestions. We have incorporated the latest item details and made corresponding adjustments to the research narrative (line 153).

3. The details of the study are very general (lines 279-296). It is not clear how many interviews were conducted and with whom, which also applies to field visits. There is no coherent method. It is also unclear how the rankings were determined (343).

Thank you very much for your suggestions.

Regarding information such as interviewees and the number of interviews, we have provided additional details in the Data Sources section (line 293-310). The specific additions are as follows:

The data in this study were collected from semi-structured interviews, secondary sources, and other channels. Primary data collection consisted of three main stages. In the first stage, we conducted field research on the energy industry development in Districts A and B to gain a preliminary understanding of the low-carbon energy transition context in these two counties. The second stage involved three rounds of semi-structured interviews: After the release of the Plan, we conducted an in-depth interview with Mr. Z, the official in charge of low-carbon energy transition policies at the Hangzhou Development and Reform Commission, to explore the background of municipal-level policy formulation, the core content of the policy, and the practical challenges faced by the two districts. Following the formal implementation of the Plan, we visited the development and reform bureaus of Districts A and B and conducted semi-structured interviews with the heads of their energy departments, Mr. H and Mr. L, respectively. These interviews focused on the districts’ assessments of the importance of different low-carbon policy tools and their evaluation criteria. In the third stage, follow-up interviews were conducted via WeChat and phone calls with the three aforementioned respondents to supplement and refine previous discussions. Additional materials, including policy documents, county-level emissions reduction data, and corporate revenue data related to low-carbon energy transition, were also obtained.

The prioritization was determined during the interviews based on local conditions and the upcoming work plans of relevant departments. This information has been added to the notes of the policy tool ranking tables for both District A and District B (line 364-366). The specific additions are as follows:

The prioritization of policy tools was determined by interviewees based on local implementation plans, while their attribute characteristics were compiled by the authors from interview transcripts.

4. It is also unclear what period the analysis covers.

Thank you very much for your suggestions.

This study focuses on the selection of policy tools by county governments during the low-carbon energy transition, covering the period from May to November 2024. Specifically:

Case study period: Post-May 2024 (after the release of China’s Carbon Peak Pilot (Hangzhou) Implementation Plan), involving in-depth interviews with heads of relevant departments. This information has been added to the Data Sources section (line 298).

Discrete choice experiment: Data collection occurred from 6 to 20 November 2024, as noted in Section 4.2 (line 542-543).

5. The analysis process itself is not explained: the authors do not explain why they include other CGs (507) – the analysis process should be explained and described in the abstract.

Thank you very much for your suggestions.

The analytical process of this study is as follows:

First, through a comparative case study of two counties in Hangzhou, the research preliminarily identified the general logic behind county governments’ energy policy prioritization. However, given the limited generalizability of case studies and the lack of empirical data supporting the existing logical framework, the study further employed a DCE to conduct a questionnaire survey and econometric analysis on local governments’ preferences for policy tool attributes. The results further validated the case study findings. We have supplemented this information in the abstract (see line 12-16). The specific additions are as follows:

Building on the theoretical framework of governmental attention allocation, the research first analyses how internal-external governmental relations shape policy tool prioritization through comparative case studies, followed by discrete choice experiment (DCE) to empirically examine local governments’ preferences for policy tool attributes under different relational conditions.

As mentioned, the inclusion of additional counties in the analysis aimed to broaden the research scope and provide more robust data support for the case study. Under China’s current administrative system, county governments, being at the same administrative level, play similar roles with comparable powers and responsibilities, leading to shared behavioural logic. To characterize their decision-making patterns, existing literature typically examines either the logic of key decision-makers or the patterns in policy documents. Given that county governments rarely issue policy documents on low-carbon energy transitions, this study adopted the first approach, focusing on the decision-making logic of officials responsible for low-carbon transition initiatives. During fieldwork, we found that low-carbon transitions involve multiple agencies, including development and reform bureaus, ecological environment bureaus, and natural resources bureaus. Thus, our survey primarily targeted staff from these departments in the selected counties. For details, see line 538-542.

6. The conclusions (577) do not follow directly from the study and are more on a general nature.

Thank you very much for your suggestions.

As suggested, we first revised the conclusion section and added the study’s main contributions, as detailed below (line 674-700):

Low-carbon energy transition serves as both the core pathway and critical enabler for achieving sustainable development. To elucidate the logic behind local governments’ policy prioritization in energy low-carbon transitions, this study draws on the government attention theory, constructs an analytical framework integrating “internal-external government relations, policy tool attributes, and policy prioritization.” Through comparative case analysis and DCE, it empirically examines the underlying mechanisms driving differentiated energy policy prioritization among CGs. The findings reveal that, influenced by local energy-intensive industries and fiscal conditions, CGs exhibit two distinct relational models in energy transitions: vertical intergovernmental relations-dominant and government-business relations-dominant. Under the two models, the preferences for policy tool attributes diverge significantly: CGs dominated by vertical intergovernmental relations adopt an “upward-looking” approach, considering policy tools’ operability, economic attributes, clarity of indicators, and political attributes to varying degrees. CGs dominated by government-enterprises relations exhibit a “downward-looking” tendency, focusing primarily on economic attributes and operability. The study uncovers the causal mechanism through which internal-external government relations shape policy prioritization: structural heterogeneity in these relations mediates local governments’ preference weighting for policy tool attributes, ultimately leading to divergent prioritization.

The main contribution of this study lies in: (1) Proposing an attention-based analytical framework that bridges “actor characteristics” and “policy attributes,” offering new insights into why peer governments adopt heterogeneous policy sequencing. (2) Combining qualitative and quantitative methods to abstract policy sequencing logic while quantitatively measuring preference variations across policy tool attributes. (3) By analyzing energy transition policies, this study advances the understanding of local governance logic in “low-incentive, low-constraint” public affairs.

Following the reorganization of the conclusion, we also attempted to refine the discussion section to strengthen its connection with the research content. The revisions primarily focus on two aspects (line 606-672):

(1) Why county-level governments prefer different policy tool attributes under varying internal and external governmental relationships. This section includes additional discussion on the interaction effects between policy tool attributes.

(2) The influence of China’s unique low-carbon governance institutional environment on the logic of energy policy tool selection. This section now incorporates a comparison of attribute preferences in energy policy choices across different countries.

The revised content is as follows:

Contrary to the prevailing literature’s assumption of homogeneous policy attribute preferences among local governments [42,63], this study, through examining energy low-carbon policy selections across CGs in Hangzhou, reveals that variations in government internal-external relations can reshape their inherent preferences for policy tool attributes. The findings demonstrate significant differences in local governments’ preference for policy tool attributes under distinct internal-external relational contexts. On the one hand, counties dominated by intergovernmental relations consider not only the clarity of indicators and political attributes but also the operability and economic attributes of policy tools. This is because these counties aim to meet regional transformation targets and gain higher-level government recognition, which requires the achievement of assigned tasks, especially in the context of a carbon peak pilot. Therefore, in addition to considering whether higher-level governments set specific indicators, these governments assess the difficulty of achieving the targets and favour policies with clear and attainable indicators. Additionally, the consideration of economic attributes is tied to China’s fiscal decentralization system. Energy low-carbon transformation is just one aspect of local governance; in a promotion system centred on GDP growth, local economic development is also a critical factor. Thus, during the transformation process, CGs cannot focus solely on targets and ignore local enterprise development and economic growth; this reflects a logic consistent with that of existing studies that advocate balancing political and economic incentives [64]. On the other hand, counties dominated by government‒enterprise relations prioritize the economic attributes and operability of policy tools, as the development demands of enterprises under economic pressure take precedence, and these counties aim to minimize the impact of energy policies on businesses. These counties typically face high energy consumption and significant enterprise emission reduction pressures, such that mandatory measures are detrimental to both short-term enterprise interests and local GDP growth. Fortunately, the higher-level government has provided a comprehensive set of policy tools for low-carbon energy transition, which creates operational flexibility for CGs in policy selection. By prioritizing policies that minimally affect enterprise production or demonstrate certain operational feasibility, CGs can achieve the objective of a “rescue curve” approach. The higher-level government, seeking to maintain steady GDP growth, often tacitly permits such practices and adjusts allocation targets within jurisdictions to ultimately fulfill objectives set by even higher authorities. Moreover, CGs emphasize tool feasibility because highly operable policies are more effective in motivating participation from energy-intensive enterprises and other stakeholders. The combination of these two attributes helps reduce political costs while amplifying policy economic benefits. Conditional logit model results further indicate that CGs tend to favour moderately operable tools because highly operable tools, while easy to implement, may not sufficiently demonstrate efforts to higher authorities, whereas low-operability tools involve high costs. Thus, moderately operable tools that balance performance and cost become the preferred choice.

It should be emphasized that the selection logic of CGs in choosing energy policy tools—based on government internal-external relations and policy tool attributes—is closely tied to China’s institutional context of low-carbon energy transition, and to some extent differs from the selection approaches in other countries and other public policy domains. On the one hand, local governments’ policy behaviors are embedded within the national political-economic system, leading to distinct priorities regarding policy tool attributes. For instance, in the U.S., where local governments enjoy greater autonomy and independent legislative power, energy transition policies diverge across federal, state, and local levels, with local governments rarely factoring in political considerations but prioritizing economic efficiency [65]. Under a multi-party competitive system, Germany’s low-carbon energy policies place greater emphasis on responding to voters’ environmental demands [66]. Meanwhile, in Australia, where energy resources are abundant, low-carbon policy choices are often captured by the interests of “elite groups” [67]. On the other hand, China’s low-carbon task currently presents weak incentives and constraints, and the lack of mechanisms reduces its importance in local governance, which leads to its neglect in policy tool selection. Compared with the emphasis on quantifiable indicators in general government actions [50], “clear indicators” or “higher-level government attention” are not highly prioritized in the energy policy tool selection of CGs, with counties dominated by government‒enterprise relations often disregarding these factors. Conversely, experimental data show that, during the overall economic downturn, CGs highly prioritize economic attributes and operability, with even counties dominated by intergovernmental relations showing significant concern for economic attributes in energy policy tool selection. This reflects both concerns over the impact of energy transformation on local economic development and the lack of incentives and constraints in low-carbon task itself, which may hinder the transition to a low-carbon economy.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This paper focuses on the policy prioritization of county-level governments in China's low-carbon energy transition, combining the theory of government attention, comparative case study analysis, and discrete choice experiments (DCEs), which makes the topic novel and of great practical significance.The authors innovatively constructed an integrated analytical framework of "internal-external government relations" and policy instrument attributes, which provides a new theoretical perspective for understanding the behavioral logic of local governments under weak incentive and constraint mechanisms.The case selection is typical (Hangzhou A and B counties), the data sources are diversified (interviews, statistical reports, policy texts), and the empirical analysis is in-depth, especially in revealing the differences in preferences for policy instrument attributes under different dominant relationships.The conclusions of the study provide an empirical basis for optimizing the synergy between central and local policies and balancing low-carbon goals and economic development, and are of high academic value and practical reference.

It should be noted that there are still a number of aspects of this paper that need further improvement as follows.

First, the literature review has not explored enough international cutting-edge literature, and some key concepts (e.g., “weak incentive constraints”) are not clearly defined, relying only on Chinese literature. It is suggested to add relevant English literature and strengthen the explanatory power of the theoretical framework.

Second, in terms of the selection of regions, it is well known that Hangzhou has achieved remarkable results in energy transition, but given the diversity of local governments in China, are the results typical? Why were only two counties in Hangzhou selected, and are they representative and valid? It is recommended that a brief comparison with other regions or a national contextualization of the case selection be added to enhance external validity.

Third, how are the “dominant relationships” in the experimental contexts, such as high/low energy consumption and difficulty/easiness, categorized? It is suggested that clearer objective criteria are needed, rather than relying on subjective judgment.

Fourth, the calculation results are only presented in the form of a table, which is not very readable and repetitive. It is suggested to add visual pictures to show the calculation results.

Fifth, the interaction effect of policy instrument attributes (e.g., how economic attributes and operability jointly affect the priority) has not been fully discussed, and the conclusion can be further extended to the optimization path of the system.

Sixth, the language presentation needs to be touched up to improve fluency (e.g., redundant sentences, terminology consistency).

Finally, the format of references needs to be revised and harmonized, while the latest studies (after 2020) are not sufficiently cited, e.g., the study of local government behavior in the low-carbon transition.

The manuscript provides important insights but requires revisions to strengthen its theoretical foundations, empirical generalizability, and policy applicability. The quality of the article will be significantly improved if targeted improvements are made.

 

Comments on the Quality of English Language

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

Author Response

This paper focuses on the policy prioritization of county-level governments in China's low-carbon energy transition, combining the theory of government attention, comparative case study analysis, and discrete choice experiments (DCEs), which makes the topic novel and of great practical significance. The authors innovatively constructed an integrated analytical framework of "internal-external government relations" and policy instrument attributes, which provides a new theoretical perspective for understanding the behavioral logic of local governments under weak incentive and constraint mechanisms. The case selection is typical (Hangzhou A and B counties), the data sources are diversified (interviews, statistical reports, policy texts), and the empirical analysis is in-depth, especially in revealing the differences in preferences for policy instrument attributes under different dominant relationships. The conclusions of the study provide an empirical basis for optimizing the synergy between central and local policies and balancing low-carbon goals and economic development, and are of high academic value and practical reference.

Thanks a lot for the reviewer's comments!

It should be noted that there are still a number of aspects of this paper that need further improvement as follows.

1. The literature review has not explored enough international cutting-edge literature, and some key concepts (e.g., “weak incentive constraints”) are not clearly defined, relying only on Chinese literature. It is suggested to add relevant English literature and strengthen the explanatory power of the theoretical framework.

Thank you very much for your suggestions.

To strengthen the theoretical framework’s explanatory power, key English literature from 2020-2025 has been supplemented and updated in the literature review section (see line 70-94).

Additionally, the concept of ‘weak incentives and constraints’ has been clarified, with this explanatory content provided in Footnote 2:

China’s current energy transition practices, particularly in low-carbon city and carbon peaking pilot programs, reveal a lack of clear policy targets or corresponding support—financial or otherwise—from higher levels of government. As a result, local governments operate in an environment of weak incentives and weak constraints for low-carbon initiatives.

2. In terms of the selection of regions, it is well known that Hangzhou has achieved remarkable results in energy transition, but given the diversity of local governments in China, are the results typical? Why were only two counties in Hangzhou selected, and are they representative and valid? It is recommended that a brief comparison with other regions or a national contextualization of the case selection be added to enhance external validity.

Thank you for your questions.

Why Hangzhou?

Hangzhou is among the few Chinese cities participating in both the Low-Carbon City Pilot and the Carbon Peak City Pilot initiatives. As a pioneer with extensive experience in low-carbon energy transition, it serves as an exemplary model for other regions and to some extent leads China’s energy transition efforts. In other words, Hangzhou’s approach reflects both the ongoing practices of China’s current transition and the “standard playbook” for the country’s long-term energy decarbonization, making it highly representative for understanding local government behavior in this context.

Why Districts A and B?

Low-carbon energy transition is not solely the task of high-energy-consumption districts. Under the municipal pilot framework, all subordinate districts are obligated to contribute. However, existing literature and municipal-level interviews reveal significant disparities in the enthusiasm and implementation strategies of districts with varying energy consumption levels. Based on comparative analyses of administrative attributes, industrial structures, and current energy consumption levels across Hangzhou’s districts, this study selects Districts A and B—which share similar administrative classifications and historical industrial profiles but exhibit stark differences in current energy consumption. Cases from other cities were excluded to isolate the impact of institutional environments on local government actions and ensure a consistent policy toolkit across research cases.

Supplementary explanations regarding the relevant contents of the case selection process have been provided in the text as suggested (line 265-292).

As suggested during review, the Discussion section now includes comparative analyses with cases from the U.S., Germany, and Australia, as detailed below (line 647-661):

It should be emphasized that the selection logic of CGs in choosing energy policy tools—based on government internal-external relations and policy tool attributes—is closely tied to China’s institutional context of low-carbon energy transition, and to some extent differs from the selection approaches in other countries and other public policy domains. On the one hand, local governments’ policy behaviors are embedded within the national political-economic system, leading to distinct priorities regarding policy tool attributes. For instance, in the U.S., where local governments enjoy greater autonomy and independent legislative power, energy transition policies diverge across federal, state, and local levels, with local governments rarely factoring in political considerations but prioritizing economic efficiency [65]. Under a multi-party competitive system, Germany’s low-carbon energy policies place greater emphasis on responding to voters’ environmental demands [66]. Meanwhile, in Australia, where energy resources are abundant, low-carbon policy choices are often captured by the interests of “elite groups” [67].

3. How are the “dominant relationships” in the experimental contexts, such as high/low energy consumption and difficulty/easiness, categorized? It is suggested that clearer objective criteria are needed, rather than relying on subjective judgment.

Thank you for your questions.

In the DCE, respondents determined their judgments regarding high-/low-energy consumption and local government leadership based on regional realities during the survey. As the respondents were primarily civil servants from county-level Development and Reform Bureaus, Natural Resources Bureaus, and Ecology and Environment Bureaus, they possessed clear professional understanding of local energy consumption patterns and low-carbon governance. Additionally, the authors cross-validated randomly selected questionnaires against actual conditions in corresponding counties using IP addresses, with results showing general consistency.

4. The calculation results are only presented in the form of a table, which is not very readable and repetitive. It is suggested to add visual pictures to show the calculation results.

Thank you very much for your suggestions.

To enhance the readability of the results, this study visualizes the conditional logit model outcomes by adopting a forest plot approach, replacing Tables 8 and 9 with Figures 2 and 3 (line 570, 590).

 

5. The interaction effect of policy instrument attributes (e.g., how economic attributes and operability jointly affect the priority) has not been fully discussed, and the conclusion can be further extended to the optimization path of the system.

Thank you very much for your suggestions.

As suggested, we have supplemented the discussion on the interaction effects between economic attributes and operational feasibility (see lines 625-646):

On the other hand, counties dominated by government‒enterprise relations prioritize the economic attributes and operability of policy tools, as the development demands of enterprises under economic pressure take precedence, and these counties aim to minimize the impact of energy policies on businesses. These counties typically face high energy consumption and significant enterprise emission reduction pressures, such that mandatory measures are detrimental to both short-term enterprise interests and local GDP growth. Fortunately, the higher-level government has provided a comprehensive set of policy tools for low-carbon energy transition, which creates operational flexibility for CGs in policy selection. By prioritizing policies that minimally affect enterprise production or demonstrate certain operational feasibility, CGs can achieve the objective of a “rescue curve” approach. The higher-level government, seeking to maintain steady GDP growth, often tacitly permits such practices and adjusts allocation targets within jurisdictions to ultimately fulfill objectives set by even higher authorities. Moreover, CGs emphasize tool feasibility because highly operable policies are more effective in motivating participation from energy-intensive enterprises and other stakeholders. The combination of these two attributes helps reduce political costs while amplifying policy economic benefits. Conditional logit model results further indicate that CGs tend to favour moderately operable tools because highly operable tools, while easy to implement, may not sufficiently demonstrate efforts to higher authorities, whereas low-operability tools involve high costs. Thus, moderately operable tools that balance performance and cost become the preferred choice.

Regarding institutional optimization, relevant content has been added to the policy recommendations section (lines 732-735):

This includes systematically increasing the weight of low-carbon energy indicators in performance evaluations and flexibly guiding local energy policy implementation through measures such as direct incentives for low-carbon energy transitions.

6. The language presentation needs to be touched up to improve fluency (e.g., redundant sentences, terminology consistency).

Thank you very much for your suggestions.

To enhance linguistic fluency, redundant sentences and terminological consistency have been reviewed and revised—for instance, standardizing ‘instrument’ to ‘tool’ and unifying the expression ‘internal-external government relations.’

7. The format of references needs to be revised and harmonized, while the latest studies (after 2020) are not sufficiently cited, e.g., the study of local government behavior in the low-carbon transition.

Thank you very much for your suggestions.

The references have been updated with new literature, reorganized, and formatted in accordance with the journal’s requirements.

Additionally, highly cited publications from 2020 to 2025 have been incorporated into the relevant literature review.

The manuscript provides important insights but requires revisions to strengthen its theoretical foundations, empirical generalizability, and policy applicability. The quality of the article will be significantly improved if targeted improvements are made.

We sincerely appreciate your valuable suggestions once more.

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for answering my questions and including my comments and remarks. No additional comments arrise.

Reviewer 2 Report

Comments and Suggestions for Authors

The revised manuscript could be regarded as reaching the publication standard.