Research on the Impact of Energy Saving and Emission Reduction Policies on Carbon Emission Efficiency of the Yellow River Basin: A Perspective of Policy Collaboration Effect

Abstract

With China’s proposed carbon peak and carbon neutrality goals, energy conservation and emission reduction are becoming increasingly urgent for the ecological protection and high-quality development of the Yellow River Basin. Based on a systematic combing through of the energy saving and emission reduction (ESER) policies in the Yellow River Basin, this paper empirically analyzed the impacts of objectives collaboration and measures collaboration of ESER policies on the carbon emission efficiency of prefecture-level cities in the Yellow River Basin, by comprehensively adopting the super-slack-based measure (Super-SBM) model and the two-way fixed-effect model. The results of the study found that: (1) with the continuous improvement in policies, the collaboration level of ESER policies in the Yellow River Basin has been significantly improved; (2) the dual-objective collaboration of ESER policies has a significant promotional effect on the carbon emission efficiency of the Yellow River Basin with a lag effect, while the impact of multi-objective collaboration is not significant; (3) the dual-measure collaboration and multi-measure collaboration of ESER policies can effectively promote the improvement in carbon emission efficiency in the Yellow River Basin. This indicates that, in terms of carbon emission reduction in the Yellow River Basin, the objective setting of ESER policies can not be too much, and should pay attention to the mutual coordination of different policy measures to strengthen the carbon reduction effect of ESER policies collaboration.

1. Introduction

As an important ecological barrier, the ecological protection of the Yellow River Basin is vital to China’s ecological security and environmental management. Although the Yellow River Basin has seen great achievements in socioeconomic development, it still faces problems such as a fragile ecological environment, serious pollution emissions and poor quality of development. As a major national strategy, the ecological protection and high-quality development of the Yellow River Basin has become the focus of social attention since 2019. In October 2021, the Central Committee of the Communist Party of China and the State Council issued the “Outline of Ecological Protection and High-quality Development Plan for the Yellow River Basin” and clearly proposed to strengthen the management of environmental pollution systems and increase the concerted treatment of industrial pollution. This has put forward higher requirements for the environmental governance practices of the Yellow River Basin in the new era, and has also brought challenges and opportunities for the energy conservation and emission reduction in the provinces (autonomous regions) along the Yellow River. Faced with the strategic opportunity, the provinces along the Yellow River should focus on “ecological protection” and “high-quality development”, and make every effort to promote energy conservation and emission reduction, taking into account the current situation of high pollution and high energy consumption.

For a long time, the industrial structure of the Yellow River Basin was dominated by the heavy chemical industry of energy, and the ecological and environmental pressure of the cities has been increasing. The number of resource-consuming, high-polluting and energy-consuming enterprises, such as coal mining, non-ferrous metal smelting, chemical manufacturing, coking and nitrogen fertilizer manufacturing, accounted for more than 60%, and generated a large amount of carbon emissions. Meanwhile, the ecological protection and environmental management in the Yellow River basin lagged behind the economic development, leading to prominent ecological and environmental problems [1]. High-quality development is known as comprehensive development covering the economy, society, resources, ecology and culture and aims to coordinate the contradiction between economic development and environmental protection [2,3]. Currently, scholars have studied the urban development issues in the Yellow River basin from the perspectives of land use [4], urban ecology [5] and industrial ecology [6], and found that the low-carbon constraints can improve urban resource utilization efficiency [7]; additionally, the level of urban carbon emissions is closely related to the development of industries such as manufacturing [8], tourism [9], and thermal power generation [10]. As the most important coal-producing and energy-consuming area in China, improving the carbon emission efficiency of the Yellow River Basin is beneficial to the achievement of China’s “double carbon” goal [11]. Scholars have studied the carbon emissions issues of the cities in the Yellow River Basin. For example, Sun et al. [12] found that the population, industrial structure, foreign investment, and urbanization level have effects on the carbon intensity of the resource-based cities in the Yellow River Basin under the carbon neutrality target; Xu et al. [13] found that, except for the economic development and industrial structure, the population density, foreign investment and resource endowment have different impacts on the carbon emission efficiency of the resource-based and non-resource-based cities in the Yellow River basin. In order to overcome the uneven spatial distribution of the carbon emission efficiency among the cities of in the Yellow River Basin [14], Zhang and Xu [15] hold that each city should implement differentiated emission-reduction programs in accordance with the local development situation. Moreover, Tan [16] and Li [17] argued that the industrial structure optimization and upgrading could alleviate the contradiction between economic development and carbon emissions in resource-based cities; Xu et al. [18] emphasized that green innovative technologies could effectively promote the achievement of urban carbon-emission reduction goals.

As an important measure to reduce energy consumption and pollution emissions, energy conservation and emission reduction make a significant contribution to the carbon emission reduction [19]. On the one hand, energy conservation and emission reduction could promote the optimization and upgrading of the industrial structure, and encourage enterprise R&D and energy-saving renovation, by the fiscal, taxation and financial measures, and thereby improve the carbon emission efficiency [20]; on the other hand, it could strengthen the public awareness of environmental protection and moral responsibility, and promote the formation of green and low-carbon production and lifestyles, through the means of publicity, education and laws and regulations, so as to reduce carbon emissions [21]. The implementation of energy conservation and emission reduction could guarantee the achievement of carbon reduction goals by saving energy, optimizing structures and green production [22]. In recent years, some scholars have begun to focus on quantitative analysis of and synergistic research into energy saving and emission reduction (ESER) policies. For example, Zhang Guoxing’s team constructed a quantitative model for ESER policies collaboration and analyzed the trend of ESER policies collaboration in Beijing–Tianjin–Hebei [23,24].

Some studies have shown that promoting energy conservation and emission reduction in the Yellow River Basin could help China achieve the “double carbon goal” [25]. Since 2009, from the central government and various ministries to the relevant departments of the provinces along the Yellow River a series of policies related to energy conservation and emission reduction have been promulgated and implemented, and the Yellow River Basin has achieved remarkable results in energy conservation and emission reduction, effectively promoting the process of carbon emission reduction in the basin. However, few scholars have studied the impact of energy conservation and emission reduction on the carbon emission efficiency of the Yellow River Basin. At the same time, the proposal of ecological protection and a high-quality development strategy in the Yellow River Basin objectively required increasing the collaborative governance of the ecological environment. In view of this, based on the systematic organization of ESER policies in the Yellow River Basin since 2009, this paper combined the measurement and analysis of objectives collaboration and measures collaboration of ESER policies in the Yellow River Basin, as well as the calculation for the carbon emission efficiency of 57 prefecture-level cities in the Yellow River Basin, and empirically analyzed the carbon emission reduction effect of ESER policies collaboration in the Yellow River Basin. The main innovation of this study is to construct a two-way fixed-effect model on the impact of ESER policies collaboration on carbon emission efficiency, and empirically test the roles of objectives collaboration and measures collaboration of ESER policies in carbon emission reduction in the Yellow River Basin. This study could provide the empirical evidence for optimizing and adjusting ESER policies in the Yellow River Basin, which is of great practical significance for improving the carbon emission efficiency of the Yellow River Basin and promoting the realization of the “double-carbon” goal in China. This paper is organized as follows: Section 2 analyzes the collaboration strength of ESER policies and measures in the Yellow River Basin; Section 3 calculates the carbon emission efficiency of 57 cities in the Yellow River Basin, by using the super-slack-based measure (Super-SBM) model; Section 4 empirically analyzes the impact of the collaboration effect of ESER policies on carbon emission efficiency in the Yellow River Basin by constructing a panel data model; Section 5 concludes with recommendations.

2. Measuring the Level of Collaboration of ESER Policies in the Yellow River Basin

“The Eleventh Five-Year Plan”, for the first time, combined energy conservation and emission reduction, and defined ESER policies as policies and measures related to energy and water conservation, reduction in SO₂ and chemical oxygen demand (COD) emissions, comprehensive utilization of industrial solid waste and development of clean energy [26]. It took four months to collect and organize the information on ESER policies and regulations involving the Yellow River basin from 2009 to 2022 through data review and research, involving ESER policies and measures issued by numerous institutions such as the National People’s Congress, the Chinese People’s Political Consultative Conference, the State Council, central ministries and commissions, and relevant departments in nine provinces along the Yellow River, totaling 894 policies and regulations.

2.1. Policy Quantitative Criteria Design

Through research and consultation with relevant experts from government departments and universities and research institutes, this paper provides scores based on the rank of the department that introduced the policy, the level of detail of the content, the strength of implementation and the degree of enforcement, and quantitatively analyses the ESES policy objectives and measures [27].

2.1.1. Policy Strength

The strength of the policy depends on the subject of the policy enactment and the type of policy, which reflects the strong degree of the government’s policy implementation. Among them, the higher the subject’s level of promulgation and implementation, the greater will be the effectiveness of the policy; the types of policies ranged from high to low policy effectiveness are laws, regulations, ordinances, regulations, decisions, opinions, approaches, standards, guidelines, rules, programs, notices, announcements, plans and norms [28]. The quantitative criteria of ESER policies are shown in

Table 1. Quantitative standards of ESER policies strength.

Score	Policy Strength Scoring Criteria
5	Laws and regulations promulgated by the National People’s Congress and its Standing Committee.
4	Various directives, regulations, provisions, as well as the orders of ministries and commissions promulgated by the State Council and local people’s congresses and their standing committees.
3	Provisional regulations and provisions, decisions, opinions, methods, standards and programs promulgated by the State Council, as well as regulations, provisions and decisions promulgated by ministries and provincial governments.
2	Action plans, opinions, approaches, guidelines, rules, programs, conditions, interim regulations and standards issued by various ministries and provincial government departments.
1	Simple notices, announcements and planning.

.

2.1.2. Policy Objectives

According to the analysis of the policy text, the ESER policy objectives can be categorized into seven types, which are pollution prevention (PP), establishing the concept of energy conservation and emission reduction (EC), improving energy utilization efficiency (IEE), promoting industrial upgrading (PI), improving the effect of energy conservation and emission reduction (IE), promoting energy conservation and emission reduction technology transformation (PE) and optimizing of energy consumption structure (OE). The contents of the policy objectives and their quantitative criteria are shown in

Table 2. ESER policy objectives content.

Type of Policy Objectives	Contents of Policy Objective
PP	Strengthen environmental monitoring, formulate environmental protection laws and regulations, and strengthen law enforcement, etc.
EC	Strengthen environmental education and guide the public and enterprises to raise awareness of energy conservation and emission reduction, etc.
IEE	Promote the use of energy-saving and environmental protection equipment, strengthen energy-saving management, promote the development of clean energy, adjust energy production and consumption patterns, etc.
PI	Technological innovation, product innovation, service upgrade and development of human resources, etc.
IE	Strengthen policy guidance, establish a sound responsibility system and assessment mechanism for ESER, etc.
PE	Promote cleaner production technologies, strengthen technological innovation and research and development, etc.
OE	Develop new energy sources, increase the use of clean and renewable energy, etc.

and

Table 3. Quantitative standards for ESER policy objectives.

Score	Policy Objective Scoring Criteria
5	Clearly put forward energy conservation and emission reduction policies, from legislation, publicity, implementation and other aspects of a comprehensive and strong guidance.
4	Clearly put forward energy conservation and emission reduction from a legislative perspective, with detailed provisions for energy conservation and emission reduction in relevant areas.
3	Promote ESER in various sectors or areas and have specific measures.
2	Clearly put forward the corresponding ESER targets, but did not propose specific measures.
1	Only the targets related to ESER are involved.

, respectively.

2.1.3. Policy Measures

Policy measures of ESER policies can be divided into seven types: administrative measures (AM), guidance measures (GM), fiscal and tax measures (FT), personnel measures (PM), financial measures (FM), technical measures (TM) and other economic measures (OE). The contents of policy measures and their quantitative criteria are shown in

Table 4. ESER policies measures content.

Type of Policy Measures	Contents of Policy Measures
AM	Government administrative licensing, supervision and inspection and approval and other mandatory means.
GM	Publicity, promotion and project demonstration, etc.
FT	Financial taxes, subsidies, etc.
PM	Personnel training, rewards and punishments, scheduling arrangements, etc.
FM	Credit, finance, etc.
TM	The government encourages research and development, application and promotion of ESER-related technologies, etc.
OE	Related to accounting for expenses, costs, prices and depreciation.

and

Table 5. Quantitative standards for ESER policies and measures.

Score	Policy Objective Scoring Criteria
5	Establishes very specific measures and methodological approaches and clearly requires the relevant subjects to enforce.
4	Relatively specific measures and methods have been developed and are mandatory, but the subject of compulsion is not specified.
3	Relatively specific measures and approaches are developed, but not required to be enforced.
2	Develop or involve relevant policies but measures and methods are not specific.
1	Only relevant policy objectives are addressed but no relevant measures and approaches are developed.

, respectively.

2.2. Policy Collaboration Model Construction

Considering the differences in policy implementation efforts and results, this paper classifies ESER policies collaboration effects into two scenarios: objectives collaboration and measures collaboration.

2.2.1. Policy Objectives Collaboration

Objectives collaboration is also divided into dual-objective collaboration and multi-objective collaboration, where dual-objective collaboration refers to the case where two policy objectives are included in the same ESER policies; multi-objective collaboration refers to the case where three or more objectives are included in the same ESER policies.

The calculation formula for the Dual Objective Collaboration (DOC) strength is:

$$DO{C}_i={\displaystyle \sum _{i=1}^{N_2}}P{D}_{jk}•P{D}_{jt}•P_j,k\ne t$$

(1)

where $DO{C}_i$ represents the dual-objective strength of collaboration of ESER policies in the $i$th year; $N_2$ represents the number of ESER policies with dual objectives issued in the $i$th year; $P_j$ represents the policy strength score of policy $j$; $P{D}_{jk}$ and $P{D}_{jt}$ represent the scores of $k$ and $t$ objectives, respectively, in the ESER policies of Article $j$, and there are $C_2^7$ kinds of collaboration in the dual-objective collaboration.

When this paper takes the three-objective collaboration as an example, the multi-objective collaboration (MOC) strength is calculated as:

$$MO{C}_i={\displaystyle \sum _{i=1}^{N_3}}\left(P{D}_{jk}\times P{D}_{jt}\times P{D}_{jl}\times P_j\right),k\ne t\ne l$$

(2)

In the formula, $MO{C}_i$ represents the multi-objective collaboration strength of ESER policies in year $i$; $N_3$ represents the number of ESER policies containing the three objectives issued in year $i$; $P{D}_{jl}$ represents the score of $l$ objectives in the $j$th ESER policies, and the rest of the symbols have the same meaning as above, and there are a total of 35 collaborative cases of the multi-objective collaboration.

By analogy, this paper can obtain a four-objective collaboration strength, five-objective collaboration strength, etc. Thus, the sum of the sums is the multi-objective collaboration strength of ESER policies.

2.2.2. Policy Measures Collaboration

Similar to the objective of policy collaboration, policy measures collaboration is also divided into dual-measures collaboration and multiple-measures collaboration. Among them, dual-measures collaboration refers to the situation when two measures are adopted at the same time in the same ESER policies, and multiple-measures collaboration refers to the situation when three or more measures are adopted at the same time in the same ESER policies.

Dual-measures collaboration (DMC) strength is calculated as:

$$DM{C}_i={\displaystyle \sum _{i=1}^{n_2}}P{M}_{jk}•P{M}_{jt}•P_j,k\ne t$$

(3)

In the formula, $DM{C}_i$ represents the dual-measure collaboration strength of ESER policies in year $i$; $n_2$ is the number of ESER policies with double measures issued in the $i$th year; $P_j$ is the strength score of ESER policies in article $j$; $P{M}_{jk}$ and $P{M}_{jt}$ represent the scores of $k$ and $t$ measures in ESER policies in Article $j$, respectively.

When this paper takes the three-measure collaboration as an example, multiple-measures collaboration (MMC) strength is calculated as:

$$MM{C}_i={\displaystyle \sum _{i=1}^{n_3}}P{M}_{jk}•P{M}_{jt}•P{M}_{jl}•P_j,k\ne t\ne l$$

(4)

where $MM{C}_i$ is the multi-measure collaboration strength of ESER policies in year $i$; $n_3$ is the number of ESER policies using three measures promulgated in year $i$; $P{M}_{jl}$ is the score of $l$ measures in ESER policies in article $j$. The remaining symbols have the same meaning as above.

And so, this paper can obtain four measures collaboration strength, five measures collaboration strength, etc. Thus, the sum of the sum is the ESER policies multi-measure collaboration strength.

2.3. Policy Collaboration Measurement of ESER

In this study, experts and scholars who have long been engaged in ESER policies and policy makers from government departments were invited to score the strength, objectives and measures of ESER policies in the Yellow River Basin, and the scoring results were summarized and compared, and the mean value was taken if the score difference was within 2 points, and those exceeding 2 points were discussed centrally and scored twice until the score difference was reduced to within 2 points.

2.3.1. Collaboration Analysis of Policy Objectives

By bringing the quantified results of ESER policy objectives into Equations (1) and (2), this paper can measure the change in objective collaboration strength of ESER policies in the Yellow River Basin from 2009 to 2022, and the results are shown in Figure 1 and Figure 2.

According to Figure 1, the trend of the change in the strength of the dual-objective collaboration of ESER policies in the Yellow River Basin provinces tends to be the same, in which the peak of the dual-objective collaboration strength occurs three times successively in 2012–2014, 2016–2018 and 2020 to date.

From 2012 to 2014, China was facing the haze event. The central government promulgated the Action Plan for the Prevention and Control of Air Pollution and the State Council proposed the most severe ten measures for the prevention and control of air pollution in history in 2013. From 2016 to 2018, China’s 13th Five-Year Plan was in the early stage, and the government put forward work plans for energy conservation and emission reduction, such as optimizing industrial and energy structures, strengthening energy conservation in key areas, deepening the reduction in major pollutants and vigorously developing a circular economy, and local governments have also launched policies related to energy conservation and emission reduction. However, in the later part of the 13th Five-Year Plan, the government at all levels made and released significantly fewer policies around energy conservation and emission reduction. Since 2020, the “double carbon” target and the strategy of ecological protection and high-quality development of the Yellow River Basin have made the provinces along the Yellow River Basin attach great importance to energy conservation and emission reduction work, coupled with the fact that it is the beginning of the 14th Five-Year Plan, governments at all levels at the national level have intensively introduced a large number of energy conservation and emission reduction-related policies. It can be seen that the changes in the strength of the dual-objective collaboration of ESER policies in the Yellow River Basin, especially the emergence of the peak, are closely related to the importance the country attaches to ecological and environmental management. Meanwhile, with the accumulation of practical experience in energy conservation and emission reduction, the ESER policies have become more clear and specific in terms of target setting, making the overall trend a fluctuating upward trend in the collaboration strength of the dual objectives of ESER policies.

According to Figure 2, before 2020, the multi-objective collaboration strength of ESER policies in the Yellow River Basin was in a slow and fluctuating upward trend (except for Ningxia, 2019), indicating that the government set a single goal in the formulation of ESER policies during this period, with a dual-objective as the main goal. However, with the continuous accumulation of practical experience in energy conservation and emission reduction, the content of ESER policies was enriched, leading to the government’s tendency to diversify in ESER policies in terms of goal setting. Therefore, there are often multiple policy objectives set in the same ESER, policies coupled with the intensive introduction of energy conservation and emission reduction-related policies since 2020, making the strength of multi-objective collaboration of ESER policies in the Yellow River Basin begin to rise rapidly and the collaboration is much stronger than the pre-2020 level.

2.3.2. Collaboration Analysis of Policy Measures

By bringing the quantitative results of ESER policy measures into Equations (3) and (4), this paper can measure the change in the collaboration strength of ESER policies in the Yellow River Basin from 2009 to 2022, and the results are shown in Figure 3 and Figure 4.

According to Figure 3, before 2016, the dual-measure collaboration strength of ESER policies in the Yellow River Basin was in a fluctuating upward trend. This was due to the fact that during the 12th Five-Year Plan, the government put a number of policy measures in place to combat the haze problem. Entering the “13th Five-Year Plan”, as China’s haze environmental problems began to improve, coupled with the economic downward pressure increment, China, compared to the “12th Five-Year Plan”, relaxed energy conservation and emission reduction work to a certain extent, highlighted by the “13th Five-Year Plan”, during which the dual-measure collaboration strength of ESER in the Yellow River Basin was on a downward trend. Entering the “14th Five-Year Plan”, the central government and the provinces along the Yellow River Basin attached great importance to energy conservation and emission reduction work, so the dual measures collaboration strength of ESER policies began to rebound.

According to Figure 4, similar to the trend of change in the multi-objective collaboration strength of ESER policies, the change in the multi-measure collaboration strength of ESER policies in the Yellow River Basin before 2020 is relatively stable and in a slow upward trend. However, after 2020, the multi-measure collaboration strength of ESER policies in the Yellow River Basin starts to rise sharply, far exceeding the pre-2020 level. It shows that, with the accumulation of practical experience in energy conservation and emission reduction, the content of ESER policies has been enriched, and the government tends to diversify not only in the setting of policy goals, but also in the application of policy measures, and the policy measures of energy conservation and emission reduction are more diversified, and the government starts to pay attention to the interplay of multi-measures in the same ESER policies, in order to achieve the policy objectives of energy conservation and emission reduction faster and more effectively.

3. Measurement and Analysis of Carbon Emission Efficiency of Prefecture-Level Cities in the Yellow River Basin

3.1. Measurement Index System Construction

This paper uses the Super-SBM model with undesired output to measure the carbon emission efficiency of prefecture-level cities in the Yellow River Basin [29]. Therefore, this paper constructs the measurement index of carbon emission efficiency of prefecture-level cities from three dimensions: input, expected output, and undesired output, based on the research of Li et al. [30] and Zhang et al. [31]. As shown in

Table 6. Measurements of carbon emission efficiency of prefecture-level cities in the Yellow River Basin.

Guideline Layer	Indicator Name	Indicator Description
Inputs	Energy Input	Total energy consumption of prefecture-level cities (million tons of standard coal)
	Labor Input	Year-end employment in prefecture-level cities (10,000 people)
	Capital Investment	Fixed asset investment in prefecture-level cities (million yuan)
Expected Outputs	Gross Regional Product	GDP of prefecture-level cities (million yuan)
Non-desired Outputs	Carbon Emissions	CO2 emissions from prefecture-level cities (million tons)

, where input indicators comprise energy inputs, labor inputs and capital inputs, where desired output indicators are gross regional product and non-desired output indicators are carbon emissions.

3.2. Data Collection and Collation

According to the division of administrative regions in the Yellow River basin by the Yellow River Conservancy Commission, this paper refers to the Yellow River Yearbook. This paper selected 57 prefecture-level cities as the study samples from the eight provinces in the basin after excluding the 4 prefecture-level cities (Linxia, Haibei, Haidong and Jiyuan) with serious missing data. The distribution of prefecture-level cities is shown in Figure 5. The upstream prefecture-level cities are from Qinghai, Gansu and Ningxia, the midstream prefecture-level cities are from Inner Mongolia, Shaanxi and Shanxi and the downstream prefecture-level cities are from Henan and Shandong.

3.3. Carbon Emission Efficiency Measurement

The carbon emission efficiency of 57 prefecture-level cities in the Yellow River basin can be measured by using Dearun3.1 software, and the statistical results based on the provincial level are shown in Figure 6. During the 12th Five-Year Plan period, the overall carbon emission efficiency of the Yellow River Basin provinces improved and developed steadily, but there is a certain degree of decline in the later part of the 13th Five-Year Plan period. The main reasons are that, during the 12th Five-Year Plan period, the country further optimized the industrial structure and eliminated a large number of backward production capacities; implemented energy-saving renovation projects such as energy saving of motor systems and energy system optimization; insisted on green and low-carbon development and strengthened the evaluation and assessment of target responsibilities; and vigorously remedied the haze pollution generated with remarkable results. Although a series of environmental protection policies such as carbon trading were implemented in the 13th Five-Year Plan period, the market mechanism was not yet fully mature, and the energy structure was not fundamentally improved; meanwhile, the government had to continue to rely heavily on traditional energy sources, due to the economic downward pressure. Therefore, during the 13th Five-Year Plan period, the carbon emission efficiency of three traditional energy provinces, Inner Mongolia, Shanxi and Shaanxi, significantly decreased; in addition, the carbon emission efficiency of Shandong also decreased, because the economic development was seriously dependent on traditional heavy industry with the slow adjustment of industrial structure.

4. Impact of ESER Policies Collaboration on Carbon Emission Efficiency in the Yellow River Basin

4.1. Panel Data Model Construction

This paper selected 57 prefecture-level cities as research samples and adopted the panel data model to empirically analyze the impact of ESER policies collaboration on carbon emission efficiency in the Yellow River Basin. The carbon emission efficiency of prefecture-level cities is the explained variable, the objectives collaboration and measures collaboration of ESER policies are the core explanatory variables, and the policy collaboration is subdivided into dual-objective collaboration, multi-objective collaboration, dual-measure collaboration, and multi-measure collaboration. In addition, this study chose the population (POP), economic development level (ECO), and science and technology input (Tech) as control variables from three levels of social–economic–technology to eliminate the influence of other factors on carbon emission efficiency. The three control variables are represented by the number of household registration population at the end of the year (10,000 people), the proportion of value added of secondary industry to GDP, and the government’s financial expenditure on science and technology, respectively. This study selected the two-way fixed-effect model for empirical analysis, according to the Hausman test and joint significance test. The models were constructed as follows:

$${CE}_{it}={\beta }_0+{\beta }_1{DOC}_{it}+{\beta }_5{POP}_{it}+{\beta }_6{ECO}_{it}+{\beta }_7{Tech}_{it}+{\mu }_t+v_i+{\epsilon }_{it}$$

(5)

$${CE}_{it}={\beta }_0+{\beta }_2{MOC}_{it}+{\beta }_5{POP}_{it}+{\beta }_6{ECO}_{it}+{\beta }_7{Tech}_{it}+{\mu }_t+v_i+{\epsilon }_{it}$$

(6)

$${CE}_{it}={\beta }_0+{\beta }_3{MDC}_{it}+{\beta }_5{POP}_{it}+{\beta }_6{ECO}_{it}+{\beta }_7{Tech}_{it}+{\mu }_t+v_i+{\epsilon }_{it}$$

(7)

$${CE}_{it}={\beta }_0+{\beta }_4{MMC}_{it}+{\beta }_5{POP}_{it}+{\beta }_6{ECO}_{it}+{\beta }_7{Tech}_{it}+{\mu }_t+v_i+{\epsilon }_{it}$$

(8)

where $\beta$ represents the influence coefficient of ESER policies collaboration on the carbon emission efficiency of prefecture-level cities; ${\mu }_t$ and ${\nu }_i$ represent the fixed effect of year and prefecture-level cities, respectively; and ${\epsilon }_{it}$ represents the random error term.

4.2. Initial Experience Judgment

In order to understand the distribution, concentration trend and dispersion of the variables, descriptive statistical analysis of the relevant variable data is conducted in this paper. The results are shown in

Table 7. Descriptive statistics of variables.

Variables	Obs	Mean	Std. Dev.	Min	Max
CE	627	0.613	0.306	0.138	1.456
DOC	627	14.109	6.61	0	32
MOC	627	67.15	42.588	28.2	485.625
DMC	627	15.509	9.052	0	41
MMC	627	129.443	197.892	22.667	1482.333
ECO	627	50.69	10.184	15.93	73.92
POP	627	448.372	273.106	74	1259
Tech	627	48,594.643	73,572.207	1338	668,363

and Figure 7. Among them, Figure 7 demonstrates the scattered relationships and trend changes between the core explanatory variables ESER policies objective collaboration and measure collaboration, and the explained variables carbon emission efficiency of 57 prefecture-level cities in the Yellow River basin. It can be seen from Figure 7 that there is a certain correlation between ESER policies objective collaboration and measure collaboration and the carbon emission efficiency of prefecture-level cities in the Yellow River Basin. The empirical tests are shown in Section 4.3.

4.3. Empirical Analysis

Table 8. Estimated results of the impact of ESER policy objectives collaboration on carbon emission efficiency in the Yellow River Basin.

	DOC		MOC
	(1)	(2)	(1)	(2)
CE	0.00474 ** (2.03)	0.00434 * (1.85)	−0.0000962 (−0.54)	−0.000126 (−0.71)
POP		−0.000178 (−0.62)		−0.000161 (−0.56)
ECO		0.00166 (0.88)		0.00207 (1.09)
Tech		0.000000261 * (1.78)		0.000000282 * (1.92)

Note: “(1)” indicates the measurement results without adding control variables; “(2)” indicates the measurement results with adding control variables; standard errors are in parentheses; the superscript “*, **” represents the statistical significance levels of 10% and 5%, respectively.

gives the results of empirical tests on the effect of ESER policy objectives collaboration on the carbon emission efficiency of prefecture-level cities in the Yellow River Basin. It can be seen from Table 8 that no matter whether the control variables are added or not, there is a positive correlation between the strength of the dual-objective collaboration of ESER policies and the carbon emission efficiency of the Yellow River Basin at the 10% significant level; the dual-objective collaboration of ESER policies have an important role in promoting the carbon emission efficiency of the Yellow River Basin. With or without the inclusion of control variables, the connection between multi-objective collaboration of ESER collaboration and carbon emission efficiency in the Yellow River Basin is unfavorable and inconsequential. This implies that it is challenging to set too many policy objectives while developing and implementing energy conservation and emission reduction policies. If not, it is difficult to define the priority or importance of different policy objectives, which causes unclear objectives in the process of policy implementation and hinders the realization of the expected effects of ESER policies.

Table 9. Estimation results of the impact of collaboration ESER policies and measures on carbon emission efficiency in the Yellow River Basin.

	MDC		MMC
	(1)	(2)	(1)	(2)
CE	0.00413 ** (2.54)	0.00435 *** (2.67)	0.000120 *** (3.42)	0.000121 *** (3.45)
POP		−0.000240 (−0.84)		−0.000163 (−0.57)
ECO		0.00217 (1.16)		0.00183 (0.98)
Tech		0.000000292 ** (2.01)		0.000000289 ** (1.99)

Note: “(1)” indicates the measurement results without adding control variables; “(2)” indicates the measurement results with adding control variables; standard errors are in parentheses; the superscript “**, ***” represents the statistical significance levels of 5% and 1%, respectively.

shows the results of the empirical tests on the effect of ESER policy measures collaboration on carbon emission efficiency of prefecture-level cities in the Yellow River Basin. It can be seen from Table 9 that before and after adding control variables, there is a significant positive relationship between both the strength of ESER policies double-measure collaboration and multi-measure collaboration and carbon emission reduction efficiency in the Yellow River Basin; both ESER policies dual-measure collaboration and multi-measure collaboration have a significant contribution to carbon emission reduction efficiency in the Yellow River Basin. It can be seen that in the process of formulating and implementing ESER policies, the more policy measures in the same approach, the stronger the effectiveness of policy implementation and the more conducive to the realization of ESER policies’ effects.

4.4. Model Testing

4.4.1. Test for Policy Lag Effect

In general, it often takes a certain amount of time from the enactment and implementation of a policy to its effects, especially the achievement of policy objectives [32]. Because of this, this study needs to test the lag effect of ESER policies, and the results are shown in

Table 10. Test of lagged effect of ESER policies.

	MDC		MMC
EEP(0)	0.00434 * (1.85)	−0.000126 (−0.71)	0.00435 *** (2.67)	0.000121 *** (3.45)
EEP(1)	0.00180 (0.73)	0.0000239 (0.13)	0.00174 (1.01)	−0.0000304 (−0.25)
EEP(2)	0.00304 (1.22)	0.0000858 (0.47)	0.000621 (0.35)	−0.000256 (−1.35)
EEP(3)	0.00557 ** (2.20)	0.000258 (1.38)	0.00167 (0.91)	0.0000406 (0.21)

Note: Standard errors are in parentheses; the superscript “*, **, ***” represents the statistical significance levels of 10%, 5% and 1%, respectively.

.

It can be seen from Table 10 that when the lag is three years, the significance level of the impact of the dual-objective collaboration of ESER policies on the carbon emission efficiency in the Yellow River Basin is improved from 10% to 5%. The dual-objective collaboration of ESER policies have a lag period of 3 years on the carbon emission reduction efficiency of the Yellow River Basin, indicating that the dual-objective collaboration has further strengthened the promotion effect on the carbon emission efficiency of the Yellow River Basin in the third year after the implementation of the energy conservation and emission reduction policy. The reason is that realizing policy objectives often involves a 3–5 year process. In addition, there is no lag in the multi-objective collaboration, double-measures collaboration and multi-measure collaboration of ESER policies. Among them, because the same policy contains too many ESER objectives, the multi-objective collaboration leads to unclear and unfocused purposes in the process of policy implementation, which ultimately leads to the inconspicuous effect of the policy, so there is no lag effect; the policy measures began to take effect on the date of the release of the ESER policies. Therefore, the promotional effect of the dual-measure collaboration and multi-measure collaboration of the ESER policies on the carbon emission efficiency of the Yellow River Basin has been produced in the year of the implementation of the policy, that is, there is no lag period.

4.4.2. Robustness Tests

To prove the reliability of the empirical results, this paper further combines the methods of replacing control variables, adding control variables, and tail reduction tests to test the robustness of the panel data measures on the basis of the policy lag effect test.

(1) Replace control variables. In the control variables, the number of household registration population at the end of the year is replaced with population density and the level of economic development is replaced with gross regional product per capita.

(2) Adding control variables. Add the primary industry, the tertiary industry, and the administrative area as control variables to test whether it will affect the results.

(3) Tail reduction test. Since singular values may lead to unstable results, the highest 1% and the lowest 1% of the data were excluded for this reason.

In summary, the results of the robustness tests of the panel data measures can be obtained, as shown in

Table 11. Robustness test results of the effect of policy collaboration on carbon emission efficiency of cities in the Yellow River Basin.

	Original Control Variables	Substitution of Control Variables	Adding Control Variables	Tailoring Test
DOC	0.00434 * (1.85)	0.00491 ** (2.13)	0.00411 * (1.74)	0.00427 * (1.82)
MOC	−0.000126 (−0.71)	−0.0000980 (−0.56)	−0.000103 (−0.57)	−0.000695 (−1.55)
DMC	0.00435 *** (2.67)	0.00449 *** (2.81)	0.00405 ** (2.42)	0.00420 ** (2.58)
MMC	0.000121 *** (3.45)	0.000105 *** (3.01)	0.000117 *** (3.16)	0.000118 *** (3.38)

Note: Standard errors are in parentheses; the superscript “*, **, ***” represents the statistical significance levels of 10%, 5% and 1%, respectively.

. As can be seen from Table 11, the four robustness tests given in this paper do not change the correlation between the four core variables and the explanatory variables; thus, the results of the empirical analysis of the impact of ESER policies collaboration on carbon emission efficiency in the Yellow River Basin given in 4.3 still hold, indicating that the estimated results of the impact of ESER policies collaboration on carbon emission efficiency in the Yellow River Basin have good robustness. Among them, the dual-objective collaboration, dual-measure collaboration, and multi-measure collaboration of ESER policies have a significant role in promoting the carbon emission efficiency of the Yellow River Basin, and the multi-objective collaboration has no significant impact on the carbon emission reduction efficiency of the Yellow River Basin.

5. Conclusions and Policy Implications

Since the “12th Five-Year Plan”, the provinces along the Yellow River have attached great importance to energy conservation and emission reduction, and carbon emission reduction in the basin has achieved remarkable achievements. This paper completes the systematic sorting and quantitative analysis of relevant energy-saving and emission reduction policies in the Yellow River Basin since 2009, and empirically analyzes the impacts of collaboration of ESER policies’ objectives and measures on the efficiency of carbon emissions in the Yellow River Basin. The results of the study show that: (1) with the continuous improvement in ESER policies in the Yellow River Basin, the policy objectives and measures continue to be diversified, and the collaboration of ESER policies continues to increase, which effectively promotes the enhancement of carbon emission efficiency in the Yellow River Basin; (2) the dual-objective collaboration of ESER policies has a significant promotion effect on the carbon emission reduction efficiency of the Yellow River Basin with a three-year lag, while the effect of multi-objective collaboration on the carbon emission efficiency of the Yellow River Basin is not significant; (3) dual-measure collaboration and multi-measure collaboration have a significant promotional effect on the efficiency of carbon emission reduction in the Yellow River Basin, but there is no lag period.

In conclusion, to promote the improvement in carbon emission reduction efficiency in the Yellow River Basin, this paper provides the following policy recommendations for the optimization and adjustment of energy-saving and emission-reduction policies in the Yellow River Basin: On the one hand, the objectives of ESER policies should be clearly defined, and the dual-objective collaboration of policies should be strengthened. Although a single policy could contain multiple ESER objectives, the problem of too many unfocused policy objectives should be avoided. On the other hand, focus on the comprehensive utilization of different ESER policies and measures, and strengthen the collaboration of multiple measures. In the process of policy formulation, the collaboration of multiple measures could be enough to significantly enhance the effectiveness of policy implementation and improve the carbon emission reduction effect of ESER policies.

From the perspectives of policy objective collaboration and measure collaboration, this study tried to subdivide the collaboration into dual-objective (measure) collaboration and multiple-objective (measure) collaboration, so as to analyze the impact of policy collaboration on carbon emission efficiency. The policy recommendations put forward have a certain reference value for the implementation and optimization of ESER policies, and could prompt government departments to formulate ESER policies with more specific objectives and stronger measures in the future. However, this study still has certain limitations, for example, due to the limitation of the number of research samples, this paper defines the policies involving three or more objectives (measures) as multi-objective (measure) policies, so it does not study the impact of three objectives (measures) collaboration and four objectives (measures) collaboration on carbon emission efficiency in the Yellow River Basin. In addition, this paper focuses on the collaboration of internal objectives or measures of policies without considering the collaboration between different policies. Therefore, in the follow-up study, the research team will focus on two aspects to break through the limitations of the current research: First, expand the number of research samples and supplement the data of three-objective (measure) collaboration or even four-objective (measure) collaboration of ESER policies, so as to enrich and improve the research on the carbon emission reduction effect of ESER policies collaboration. Second, considering the collaboration between different policies, the impact of objective collaboration and measure collaboration between ESER policies on carbon emission efficiency is further studied.