1. Introduction
Since the reform and opening-up policy in the late 1970s, China’s economy has been experiencing a golden period of rapid development, which has been hailed globally as an “economic miracle”. However, frequently occurring environmental emergencies and a decline in ecological environmental quality are expected to emerge from this rapid development and become a bottleneck that restricts the economic and social development of China. The China Statistical Yearbook shows 17,442 environmental emergency events from 2000 to 2020 [1] that seriously threatened people’s safety, property and the ecological environment. For example, environmental emergencies that are caused by the improper management of urban solid waste can easily lead to environmental pollution, such as soil, water and atmosphere pollution, and threaten people’s lives and health [2]. China built its environmental emergency management system late and it is still not complete. The government performances in responding to environmental emergencies or eliminating significant environmental risks are not satisfactory. This paper aims to provide insights into the ways of building local government environmental governance capabilities in China.
Environmental emergency management is the last line of defense for environmental security. As a new research direction with strong correlations with the field of public management, environmental emergency management has been widely studied by researchers and has produced fruitful results. The existing research on emergency management has contributed to research on environmental emergency management through the discussion of the frameworks or architectures that are used in emergency management, such as “one case, three systems” [3,4,5,6], which is the emergency management system in the USA, EU and other countries [7,8,9], and emergency decision-making [10,11,12,13]. By extending these studies, researchers have explored environmental emergency management plans [14], coordination mechanisms [15,16], early warning systems [17] and governance systems [18] for environmental emergency management, but little literature has discussed how to construct an assessment of local government environmental emergency management capability within a dynamic pre–during–post process, which is explored in this paper.
Local governments are a main force and their environmental emergency management capability is critical for dealing with environmental emergencies [19]. Environmental emergency management capability is an essential indicator of local government environmental governance levels and emergency management effectiveness. Therefore, this paper is dedicated to answering the following questions: What is the connotation of the concept of the environmental emergency management capability of local governments? Which indicator system should be constructed for the measurement? Are there regional differences in local government environmental emergency management capability across China?
Drawing on the crisis management 4R theory [20], PPRR emergency management theory [21,22], the crisis life cycle theory [23] and ISO 22320 [24], this research constructed a local government environmental emergency management system with four capabilities: preparedness, early warning, response and recovery. We applied a confirmatory factor analysis to the local government environmental emergency management capability and the reliability and validity levels of the results supported the dynamic framework of that capability. We further conducted an empirical evaluation to explore regional differences in the environmental emergency management capability of local governments across China. We found that local government environmental emergency management capability in the eastern region of China is generally better, while those in the central, western and northeastern regions are relatively lacking.
2. Conceptual Framework
2.1. Local Government Environmental Emergency Management Capability
An environmental emergency refers to a course of environmental events that deviates from planned or expected actions and endangers or adversely affects humans, property or the environment and results in significant damage, loss or destruction [25]. Humans need to manage such environmental emergencies to mitigate or prevent potential losses. With the explicit goal of protecting life, property and the environment, environmental emergency management draws upon expertise from a wide array of areas, spanning science, technology, planning and management. As a fundamentally cooperative undertaking, it involves organizing collective frameworks and steering collaborative practices to address environmental problems and conflicts [17,26].
Previous literature has identified three key rationales behind environmental emergency management frameworks. The first conceives environmental emergency management as a form of regulation that is aimed at achieving specific outcomes related to environmental management or protection [27]. In contrast to the first rationale, the second holds that environmental emergency management is a process that involves negotiations between governmental and non-governmental actors, which often have different or even conflicting interests or perspectives. To enable collaborative action between these stakeholders, a channel of communication needs to be put in place to bridge or integrate their diverse interests and perspectives [28]. The third rationale argues that environmental governance arrangements need to be adaptive to be effective [29]. This is because environmental governance often needs to deal with complex dynamic processes within natural systems and the impact of human intervention is uncertain. Collectively, these conceptions imply that environmental emergency management requires dynamic adaptation and integration capabilities before it can be successfully implemented.
Different modes of environmental emergency management involve different capabilities of the actors and represent different ways of organizing and regulating collective action. For example, Hernandez and Serrano [30] used knowledge-based models to deal with flood emergencies, while Kourniotis [31] applied expert systems to assist in decision-making for the containment of chemical accidents. On the other hand, environmental emergency management includes various forms of collective decision-making that require the active participation of government actors [19,32]. Wallington et al. [33], for example, discussed a form of governance in which the central government “takes the lead” on centralized governance while local governments are responsible for decentralized governance. In this paper, we extend the previous literature by discussing environmental emergency management as an agent for governance capacity building.
Governance arrangements are ad hoc arrangements that combine the use of coalition building, discourse, rules and power to guide collective action [34]. Given the current practices and emergency management requirements of the ISO 22320 incident response, there is an exigent need for cross-system risk assessments and a shift to whole-process management. Local emergency management departments are usually responsible for planning how best to mitigate, prepare for, respond to and recover from anticipated environmental emergencies. For instance, earthquakes, tornadoes, major floods and storms, severe hurricanes and cyclones and other wide-ranging natural disasters are expected, planned for and mitigated to some extent. The scope of such disasters, however, can lead to crises that demand a swift response and immense resilience. The key is to be able to provide the necessary resources, prior training, public awareness and even political discipline to prevent major events from affecting the system in functionally unexpected ways [35,36]. It is in this spirit that this paper considered capabilities related to contingency planning and practices during disaster mitigation and post-disaster (i.e., recovery) phases.
This paper conceptualized the creation and application of a local government emergency management system as a dynamic process of knowledge, values, social order generation and resource allocation. Accordingly, we constructed a process-based framework based on the literature and classified processes into three categories: the pre-, during and post-emergency phases. Each stage involves different capacities that provide services or resources, domain-specific problem solutions, software, facilities and products for environmental emergency management [16,17,18,37]. The framework has several important characteristics (see Figure 1) and requires different agents, such as emergency prediction, response, preparedness and recovery agents, to meaningfully coordinate activities. Out of the processes introduced in this framework, actions during the pre-emergency phase are the most closely and visibly related to preparedness and early warning capabilities, whereas responses comprise the ability of actors to mobilize resources to achieve concrete results during emergencies. Environmental emergencies create crises and damage that require resilience after the events. The analytical framework that was elaborated and applied in this paper is more analytically sensitive to the dynamic emergency management framework because it combines the advantages of the 4R theory, PPRR emergency management theory, crisis life cycle theory and ISO 22320 for crisis management. As such, it conceptualizes specific governance capabilities as a corollary of the complex interplay between preparation, early warning, response and recovery.
2.1.1. Preparedness Capability
A key aspect of an environmental emergency management framework is preparation for environmental emergencies. Large-scale environmental emergencies require large and diverse resources, including real-time information, experienced personnel, appropriate facilities and advanced problem-solving skills [38], all of which demand that governments be well prepared for such emergencies. A process-based resource discovery mechanism was proposed in this study, which uses information technology to find relevant resources, design environmental risk response systems and formulate emergency plans that enable governments to be ready for environmental emergencies [39]. This mechanism requires environmental governance arrangements to be adaptive. Adaptive governance involves the ability to “understand environmental change”, “use this understanding to inform decision making” and then “act on decisions in a manner that sustains resilience of desirable system states” [40].
Moreover, it includes the ability to “review and adapt decisions as new information becomes available” [41]. Therefore, gathering knowledge by monitoring and learning the impacts of ecosystem changes and management interventions within natural systems is considered a key aspect of adaptive preparedness governance [42,43,44]. At the normative level, adaptive governance requires the willingness to adjust or modify decisions in response to changing circumstances. Emergency preparedness planning is essential for ensuring the continuity of local government operations, especially in terms of supporting response and recovery efforts. It must ensure the effective coordination of response and recovery efforts. Therefore, “H1: Preparedness Capability” was chosen as one of the four components of local government environmental emergency management.
2.1.2. Early Warning Capability
An information-based professional environmental warning system is essential for environmental governance. Considering the breadth of systemic environmental risks and the limited management resources, it makes sense to establish a priority management list and identify key items that should be regulated in advance. There is broad consensus in the literature that the creation, mobilization and utilization of environmental emergency knowledge are key issues for early warning systems [45,46,47]. One such concept is the “knowledge system”, which refers to a social system that includes knowledge claims, groups of actors and different methods for knowledge creation and exchange; these factors together form a particular worldview. The mandatory disclosure of environmental risk information would put additional public pressure on polluters and force environmental authorities to reduce risks. For example, in case of “no emergency at hand”, the prediction system would be activated to estimate the probability of a disaster occurring in the next moment and notify the emergency preparedness agency to propose measures to deal with the impending disaster in advance [28].
In situations where the consequences are high and the cost of prevention is low, it can be helpful to identify anything that could go wrong. It is important to exercise caution when facing possible threats. Environmental governance may involve learning about the impacts of environmental emergencies, protection or escapes, prior training, public awareness and bringing together knowledge from a wide range of scientific and non-scientific sources [48]. For example, a principle may say the following: “When in doubt, take protective action”, which was a lesson followed by Chinese county officials when they warned residents to vacate their homes before the 1976 Tangshan earthquake. As officials try to anticipate and prepare for all risks, heightened attention and precautions can stall them and they may take unnecessary action when faced with a highly unlikely threat. Therefore, “H2: Early Warning Capability” was chosen as one of the four components of local government environmental emergency management.
2.1.3. Responsiveness
Preparation and warnings are critical, but the ultimate test is how local governments respond when an emergency occurs. After the occurrence of an environmental emergency, local governments grade the emergency response according to the severity and development trend of that environmental emergency and organize all relevant departments to carry out on-site pollution disposal, the relocation of personnel, medical rescue, emergency monitoring, information release, maintaining social stability, etc. At its core, environmental emergency management generally means responding to all emergencies, regardless of their severity, scope or expectations. Since many local government agencies, personnel and resources are involved in planning for and response to emergencies, local governments must incorporate the prediction and management of emergencies into their overall development and management plans. Local government officials must deal with the immediate consequences of disasters, coordinate the formulation and execution of comprehensive disaster response plans, respond to citizen demands and provide additional resources and personnel, showcasing their commitment to promoting public interest and improving professional standards [49]. Additional resources based on emergency management plans and protocols may add more stringent requirements. Nonetheless, efficient decision-making and problem-solving protocols must be put in place, along with the selective use of practices, to help responders to adapt to specific disruptions to the emergency response infrastructure [5].
Emergency response agents coordinate with emergency assessment and resource discovery and allocation agents to develop strategies for mitigating the effects of ongoing emergencies [50]. For example, local governments must be familiar with the state regulations on disaster planning and mitigation, budgeting, zoning, taxation, public works management and transportation. They must provide various emergency services (such as police, fire and emergency medical services), the protection of public goods and support services (such as maintaining traffic flows). The agents responsible for resource discovery and allocation search for the relevant resources that are required and deploy the resources according to the topology of the transport network. Therefore, “H3: Responsiveness” was chosen as one of the four components of local government environmental emergency management.
2.1.4. Recovery Capability
The specific capabilities that are highlighted after an environmental emergency focus on the recovery phase. The recovery backup system for environmental emergency management has to improve error tolerance, decentralize network coupling and widely disperse and dilute risks [51]. Local officials need to ensure that all sectors provide essential services that are critical to local recovery efforts and the normal functioning of the social and economic environment [52]. It bears noting that disasters can lead to all types of changes. For example, an impending hurricane can require solutions for resource allocation, grading and evacuation methods. When time frames are longer and solutions to crises need to be learned and redesigned, as in cases of illness and psychological trauma, temporary changes to systems may be required [53]. Therefore, in practice, improving resilience against systemic environmental risks should involve competencies from the recovery phase. For example, engineering projects, such as ecosystem restoration and risk inspection, are the most immediate measures for mitigating environmental risks and improving risk resilience. Green financial instruments, such as liability insurance, within the economic system can help to maintain economic resilience in the face of damaging environmental risks. Therefore, “H4: Recovery Capability” was chosen as one of the four components of local government environmental emergency management.
3. Materials and Methods
3.1. Questionnaire
We sent out an expert sample survey to 75 environment emergency management officials from Zhejiang province who had experience of working with environmental emergency events, such as flooding and hurricanes. In this survey, officials were asked to provide extensive comments on the four capacities, covering a range of competencies within environmental emergency management. The gender ratio of male respondents to female respondents was 85.5% to 14.5%. For the age distribution, more than half of the respondents were middle-aged (aged 40 to 60), accounting for 56.5%, which is similar to the age distribution of civil servants in China. In terms of academic qualifications, 66.1% of the respondents had bachelor’s degrees and 27.4% had graduate degrees or above, which indicated that the respondents had an above-average level of education and could better understand the questionnaire. Each participant was asked to select from a list of competencies those to which they felt they belonged to some extent and many protocols needed to be adjusted or improvised. We provided semi-forced-choice schemes as alternatives for generating the empirical data.
Over an eight-week period, we contacted potential survey participants via email and/or phone to follow up and remind them to return their completed surveys as soon as possible. Of the 75 emergency management officials who were contacted, 62 supplied complete and valid responses. The demographics of the respondents showed that they were generally male, older, experienced in government and emergency management, had experienced more than one environmental crisis in their careers and had significant responsibilities. Respondents were given 37 options based on the 4R theory of crisis management, PPRR emergency management, crisis life cycle theory and ISO 22320. They were asked to choose one value from five to ten competencies that they felt would best fit the four competencies: preparedness, early warning, response and recovery. SPSS 23.0® software was used for the exploratory factor analysis to test the validity of the questionnaire. The KMO (Kaiser–Meyer–Olkin) value was 0.927 and the Bartlett sphericity test p-value was 0.000.
Data from the questionnaire survey of grassroots officials in Zhejiang province were extracted and analyzed. We used the principal component analysis method to conduct the exploratory factor analysis. The test results showed that of the four extracted factors (preparedness, early warning, response and recovery capabilities) in local government environmental emergency management, the y11 to y16 competencies were classified as one factor to evaluate the preparedness capability, the y21 to y25 competencies were classified as one factor to evaluate the early warning capability, the y31 to y38 competencies were classified as one factor to assess the response capability and the y42 to y45 competencies were classified as one factor to evaluate the recovery capability. This was consistent with the four dimensions of local government environmental emergency management capacity derived from the theory (see Table 1).
From the perspective of the cumulative variance contribution rate, the variance contribution rates of the four capabilities (preparedness, early warning, response and recovery) were 28.418%, 20.515%, 18.294% and 14.516%, respectively. The cumulative variance contribution rate was 81.743% and showed that the extracted common factors had strong explanatory power. The analysis of the test samples showed that the questionnaire on local government environmental emergency management capability had a high validity. We applied the Cronbach’s α coefficient to run the reliability test. The results showed that the Cronbach’s α coefficient of the questionnaire was 0.975. The Cronbach’s α coefficients of the four dimensions (preparedness, early warning, response and recovery) were 0.812, 0.942, 0.963 and 0.935, respectively. Therefore, the questionnaire on the local government emergency management capability and its four dimensions had a high reliability.
3.2. Sample Selection and Data Collection
It was necessary to further determine the survey objects to design a questionnaire of higher efficiency and reliability. County-level governments hold an important position within the national environmental emergency management system. Environmental emergencies often occur at the grassroots level and county-level governments are important for responding to those environmental emergencies. Therefore, county-level government officials were selected as the research subjects. The questionnaire was distributed through interviews, emails and telephone calls. From July 2020 to October 2020, we distributed 800 questionnaires and collected 791 completed questionnaires, of which 784 were valid.
4. Empirical Analysis
4.1. Confirmatory Factor Analysis
This research used AMOS23.0® software (IBM in New York, NY, USA) to implement a confirmatory factor analysis on local government environmental emergency management capability. The measurement model included four latent variables: preparedness, early warning, response and recovery capabilities. The fitting results showed that the p-value of each path was 0.000 and that the standardized path coefficients were all above 0.65, indicating that the theoretical indicators could effectively measure the level of local government environmental emergency management. However, the model fit test did not meet the standard and only seven indicators met or were close to the standard. According to the correction index of each parameter of the measurement model, the co-variation relationships between the measurement errors of indicators, such as e1 and e2, e3 and e4, e5 and e7, e8 and e10, e16 and e18, were corrected and the revised confirmatory factor analysis model was obtained (Figure 2).
The fitting effect of the revised measurement model was greatly improved and the fit index reached or was close to the model fit standard (RMSEA = 0.049 < 0.08; GFI = 0.934 > 0.90; AFI = 0.917 > 0.90; NFI = 0.971 > 0.90; RFI = 0.966 > 0.90; IFI = 0.981 > 0.90; TLI = 0.978 > 0.90; CFI = 0.981 > 0.90; PGFI = 0.849 > 0.50; PNFI = 0.840 > 0.50; PCFI = 0.849 > 0.50; CN = 337). The results showed that the confirmatory factor analysis measurement model had good fit and external qualities. The normalized load coefficient of each path coefficient was above 0.65, the p-value was 0.000 and there were no large standard errors (between 0.02 and 0.035) (Table 2).
In terms of the relationships between the latent variables, the correlation coefficients between “preparedness”, “early warning”, “response” and “recovery” were all above 0.75 and the p-value was 0.000, indicating that there was a strong correlation between the four latent variables. There could be another higher-order public latent variable above the four latent variables. They jointly measured the same latent variable: local government environmental emergency management capability. Therefore, using a measurement model (CFA) for the study was more appropriate.
According to the multivariate correlation analysis, the multivariate correlation coefficients of the remaining measurement indicators, except for y13, y14 and y15, were all above 0.50. The results indicated that the latent variables could explain at least 50% of the variance in most measurement indicators. The composite reliability (CR) of the four dimensions of local government environmental emergency management capability was above 0.9 (preparation = 0.919; early warning = 0.941; response = 0.974; recovery = 0.957). The average variance extracted (AVE) was higher than 0.65 (preparation = 0.656; early warning = 0.762; response capability = 0.822; recovery = 0.847). These results indicated that the confirmatory factor analysis model had a good inherent quality.
4.2. Descriptive Analysis
This section presents the descriptive statistical analysis of the scores for each dimension of local government environmental emergency management capability using the arithmetic mean method. Table 3 shows that the average arithmetic score of each competency exceeded 4.0 points (the total score was 7), except for “the areas with the environmental emergency have a good culture of environmental emergency management” and “the areas with the environmental emergency have the relatively developed environmental emergency industry that can provide high-quality environmental emergency products”. In other words, the scores for local government environmental emergency management capability were in the upper-middle level. The competency of “the government has good ability to mobilize the people to join in involving the environmental emergency” obtained the highest score and the competency of “the government is getting better at using experts and big data technology to help deal with environmental emergency” also reached a high level. However, the competency scores for “the areas with the environmental emergency have a good culture of environmental emergency management” and “the government actively educates the people the environmental emergency management knowledge and implements simulation exercises” were low. This showed that the respondents thought that China has a solid social mobilization ability, especially in the face of environmental emergencies and crises, across the country. In contrast, local governments did not perform well within the culture of environmental emergency management, so local governments should actively adopt diversified methods to create a cultural atmosphere of environmental emergency management and enhance citizen awareness of environmental emergency management. From the perspective of regional distribution, the scores for various measurement indicators in the eastern region were higher than those in the central, western and northeastern regions.
In conclusion, the score for the local government environmental emergency management capability and the scores for each dimension (preparedness, early warning, response and recovery) produced similar results. The eastern region had the highest score and the scores of the central, western and northeastern regions were relatively close. The above results showed the regional differences in local government environmental emergency management capability across China.
4.3. Variance Analysis
Before the variance analysis, we first examined the homogeneity of the variance test for local government environmental emergency management capability in different regions. The test results showed that the p-value was 0.395 and that local government environmental emergency management capability was homogeneous for the eastern, central, western and northeastern regions. The results of the variance analysis showed that the p-value was 0.000, indicating that the score distribution of local government environmental emergency management capability was statistically significant for the eastern, central, western and northeastern regions. We applied a post-test of the variance analysis on the premise of the homogeneity of variance (see Table 4). The test results in the LSD column show that the mean difference between the eastern region and the central, western and northeastern regions was positive and that the p-value was 0.000, indicating that the score for the eastern region was higher than those for the central, western and northeastern regions and that there was a significant difference between their capabilities. Similarly, the difference between the scores for the central and northeast regions was also significant. In contrast, the difference between the scores for the central region and the western region and between the scores for the western region and the northeast region were not significant.
As the correlation between the latent variables (preparedness, early warning, response and recovery) was significant, we used the multivariate analysis of variance to test the differences between the scores for each dimension of local government environmental emergency management capability in the different regions. The results of the Levene’s test showed that the covariance matrix had a homogenous level across the different dimensions (p-values for preparedness, early warning, response and recovery were 0.238, 0.447, 0.868 and 0.099, respectively). The results of the multivariate analysis of variance showed that the p-value obtained using the four different test methods listed in the area column (regional distribution) was 0.000, which meant that the dimensions of local government environmental emergency management capability had significant differences in the central, western and northeastern regions (see Table 5).
The post hoc test of the multivariate analysis of variance showed that in terms of preparedness, the mean difference between the eastern region and the central, western and northeastern regions was positive and that the p-value was 0.000, indicating that the score for the eastern region was higher than those of the central, western and northeastern regions. These regions had significant differences in local government environment management capability. The results were also the same for the central and northeastern regions. However, the p-value of the mean difference between the central and western regions was 0.702, indicating that the difference in scores for the central and western regions was not significant. We obtained the same conclusions from the analysis of the dimensions of preparedness, early warning, response and recovery.
4.4. Findings
The confirmatory factor analysis for local government environmental emergency management capability showed that the reliability and validity levels reached the corresponding standards, so the theoretical indicators and the corresponding questionnaire could effectively measure local government environmental emergency management capability. In addition, the overall fit of the structural equation model was good and the external quality of the measurement model was high. This showed that the confirmatory factor analysis results were consistent with the theoretical deduction, so we were able to divide local government environmental emergency management capability into the preparation, early warning, response and recovery dimensions, which were scientific and reasonable. There was a strong correlation between the preparedness, early warning, response and recovery capabilities, indicating that the four capabilities could exist objectively and operate collaboratively.
Combining with the results of the descriptive statistical analysis and the variance analysis, we found that local government environmental emergency management capability in the eastern region were generally better, while those in the central, western and northeastern regions were relatively low. This was because local government environmental emergency management capability is endogenous and closely related to economic development. The eastern region has the advantages of preferential policies and a high resource allocation efficiency, so this region has become the “first echelon” in China, with high-quality economic development and a high local government environmental emergency management capability. Affected by the spillover effect and radiation from the economy in the eastern region, the central region is the second echelon in the economy and in terms of local government environmental emergency management capability. However, most central, western and northeastern regions are in inland and border areas where economic development is relatively low and supporting facilities are insufficient, so their local government environmental emergency management capability is relatively low. Therefore, we found that the local government environmental emergency management capability in China has regional non-equilibrium characteristics and that there is significant room for improvement.
5. Conclusions and Policy Implications
5.1. Conclusions
Based on the emergency management 4R theory, PPRR emergency management theory, crisis life cycle theory and ISO 22320, we defined local government environmental emergency management capability as four dynamic dimensions. They are the preparedness, early warning, response and recovery capabilities. We applied a confirmatory factor analysis model to confirm that the classification standard of the four capabilities strongly correlated with environmental emergency management. We found that China’s local government environmental emergency management capability is generally at an upper-middle level. In detail, the local governments have a solid social mobilization ability in the face of environmental emergency events, but they do not develop a good environmental emergency management culture. We also analyzed the differences in local government environmental emergency management capability across China. We found that the eastern region had a higher environmental emergency management capability, while those of the central, western and northeastern regions were close to each other, showing a decreasing distribution in the east–central–west–northeast region. Similar results were also obtained for the four dimensions of preparedness, early warning, response and recovery capabilities across China.
5.2. Policy Implications
The performance of local government environmental emergency management may affect the results of local government environmental governance and its sustainability. Once an environmental emergency event occurs, the local government environmental emergency management capability is directly related to the safety of people’s lives and property and the quality of the ecological environment. For example, if municipal solid waste is not disposed of in time, it leads to an ecological imbalance of land, water and air pollution [54,55,56,57]. In general, to improve the local government environmental emergency management capability and reduce the negative impacts and ecological damage caused by environmental emergencies, the local governments in China should improve their environmental emergency management in the following aspects.
Firstly, China should reshape the concept of environmental emergency management as a dynamic process, including pre-, during and post-emergency stages, and pay attention to balancing the distribution of efforts and resources throughout the whole process. Environmental emergency management is a complex adaptive system that maximizes collective effectiveness. All preparation, early warning, response and recovery capacities need to be emphasized. A poor performance in any stage may lead to a significant failure in environmental emergency management practices. For example, in an emergency response stage caused by solid waste, the poor disposal of solid waste by city governors may result in serious health and environmental problems, such as unpleasant odors and the risk of explosion in landfill areas, as well as groundwater pollution because of leachate infiltration [58]. Our empirical evaluation showed that local government environmental emergency management scores in the preparation and early warning stages were lower than those in the response and recovery stages. This indicates that local government environmental emergency management focuses more on emergency response and pays less attention to preparation for and early warnings of emergencies. Therefore, local governments should balance their allocation of efforts and resources throughout the whole process of environmental emergency management. For example, local governments should pay more attention to simulation exercises, the cultivation of environmental emergency culture and the development of environmental emergency industries. In addition, local governments should apply standard environmental emergency management policy tools and real-time updates of environmental emergency management plans and enhance their pertinence and operability to prevent environmental emergency risks. Moreover, local governments should pay more attention to the comprehensive monitoring, effective identification and early warning of environmental risks, as well as playing the front-end “starter” function of environmental emergency management to prevent and avoid emergencies.
Secondly, China needs to promote the coordinated regional development of environmental emergency management capability to narrow the gap between the eastern, central, western and northeastern regions. Affected by multiple factors, such as economic development and environmental emergency management talents, local government environmental emergency management capability in the eastern region is generally more potent than those in the central, western and northeastern regions. However, environmental emergencies have spillover characteristics, such as emergencies caused by solid waste that can cause cross-regional air and water pollution [59]. Environmental governance requires holistic governance from a global perspective. Therefore, the central government should conduct the overall planning to improve local government environmental emergency management capability. For example, the central government should establish an equalization-oriented public finance system for environmental emergency management and provide appropriate policy preferences to the central, western and northeastern regions in terms of special transfer payments for environmental emergency management and the training of environmental emergency personnel. In addition, it is also possible to improve the environmental emergency management experience exchange and learning system. For example, the government should arrange for environmental emergency management officials and experts in the eastern region to serve in the central, western and northeastern regions and guide the practices of environmental emergency management in those regions.
Thirdly, the local government in China should use technology empowerment to improve local government environmental emergency management capability. For example, local governments should actively adopt big data technology within environmental emergency management for emergencies that are caused by solid waste [60], establish a comprehensive platform for environmental emergency management information systems, quickly collect, analyze and share real-time information on environmental emergencies and use experimental virtual social platforms to simulate environmental emergencies. That way, the local governments could obtain accurate data and support for early warnings, the preparation of emergency materials and emergency team deployment, thereby truly improving local government environmental emergency management capability. Local governments should also fulfill the supporting role of experts in environmental emergency management because experts play a positive role within environmental emergency management by providing rational cognition, decision-making reference and positive public opinion guidance for disseminating environmental emergency information.
This study had some limitations. The study did not conduct long-term data tracking to obtain panel data, but rather collected cross-sectional data over a short period to analyze local government environmental management capability, so we did not explore the temporal and spatial evolution of local government environmental emergency management capability. The research only provided a preliminary design for constructing and evaluating local government environmental emergency management capability using empirical data and did not discuss the mechanisms affecting those capabilities. Future work could include collecting panel data to describe the temporal and spatial evolution of local government environmental emergency management capability.
Conceptualization, B.F.; writing—original draft, L.T.; writing—review and editing, C.L. and G.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Informed consent was obtained from all subjects involved in the study.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The rotation component matrix of the revised dimensions of local government environmental emergency management capability.
| Indicator | Capabilities | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| y11 | 0.660 | |||
| y12 | 0.692 | |||
| y13 | 0.640 | |||
| y14 | 0.732 | |||
| y15 | 0.777 | |||
| y16 | 0.559 | |||
| y21 | 0.765 | |||
| y22 | 0.846 | |||
| y23 | 0.781 | |||
| y24 | 0.837 | |||
| y25 | 0.642 | |||
| y31 | 0.744 | |||
| y32 | 0.763 | |||
| y33 | 0.763 | |||
| y34 | 0.760 | |||
| y35 | 0.733 | |||
| y36 | 0.762 | |||
| y37 | 0.752 | |||
| y38 | 0.750 | |||
| y42 | 0.707 | |||
| y43 | 0.690 | |||
| y44 | 0.713 | |||
| y45 | 0.699 | |||
Path coefficients of the modified confirmatory factor analysis model.
| Indicator | Estimates | Standardized Estimates | Sd | C.R. | Sig. | |
|---|---|---|---|---|---|---|
| y11 | Preparedness | 1 | 0.903 | |||
| y12 | Preparedness | 0.847 | 0.803 | 0.025 | 33.332 | *** |
| y13 | Preparedness | 0.861 | 0.781 | 0.030 | 28.843 | *** |
| y14 | Preparedness | 0.735 | 0.646 | 0.035 | 21.174 | *** |
| y15 | Preparedness | 0.878 | 0.754 | 0.032 | 27.142 | *** |
| y16 | Preparedness | 1.072 | 0.909 | 0.027 | 39.824 | *** |
| y21 | Early Warning | 1 | 0.907 | |||
| y22 | Early Warning | 0.777 | 0.830 | 0.024 | 32.562 | *** |
| y23 | Early Warning | 0.935 | 0.894 | 0.024 | 38.811 | *** |
| y24 | Early Warning | 0.868 | 0.870 | 0.024 | 36.214 | *** |
| y25 | Early Warning | 0.974 | 0.840 | 0.029 | 33.662 | *** |
| y31 | Early Warning | 1 | 0.923 | |||
| y32 | Early Warning | 0.928 | 0.866 | 0.024 | 38.794 | *** |
| y33 | Response | 0.974 | 0.929 | 0.020 | 47.722 | *** |
| y34 | Response | 1.013 | 0.942 | 0.02 | 50.163 | *** |
| y35 | Response | 0.837 | 0.877 | 0.021 | 40.081 | *** |
| y36 | Response | 0.997 | 0.930 | 0.021 | 47.912 | *** |
| y37 | Response | 0.875 | 0.867 | 0.023 | 38.848 | *** |
| y38 | Response | 0.973 | 0.901 | 0.022 | 43.271 | *** |
| y42 | Recovery | 1 | 0.932 | |||
| y43 | Recovery | 0.984 | 0.918 | 0.021 | 46.347 | *** |
| y44 | Recovery | 0.942 | 0.917 | 0.020 | 46.220 | *** |
| y45 | Recovery | 0.977 | 0.915 | 0.021 | 45.913 | *** |
Note: *** The mean difference was significant at the 0.01 level.
Arithmetic means of the dimensions of local government environmental emergency management capability.
| Dimension | Indicator | Mean | Eastern Region Mean | Central Region Mean | Western Region Mean | Northeast Region Mean |
|---|---|---|---|---|---|---|
| Preparedness | y11 | 4.3495 | 5.0204 | 4.3153 | 4.1979 | 3.8046 |
| y12 | 4.1569 | 4.9031 | 4.1577 | 3.9635 | 3.5287 | |
| y13 | 4.2615 | 4.7398 | 4.3063 | 3.9427 | 4.0172 | |
| y14 | 3.7819 | 4.2117 | 3.5255 | 3.6927 | 3.6207 | |
| y15 | 3.9617 | 4.7092 | 3.955 | 3.6562 | 3.4655 | |
| y16 | 4.6224 | 5.2857 | 4.5946 | 4.4531 | 4.0977 | |
| Early Warning | y21 | 4.4745 | 5.2092 | 4.5045 | 4.2865 | 3.8161 |
| y22 | 4.2423 | 5.1173 | 4.2117 | 4.026 | 3.5345 | |
| y23 | 4.3418 | 5.1888 | 4.3694 | 4.0937 | 3.6264 | |
| y24 | 4.2296 | 5.0714 | 4.3378 | 3.901 | 3.5057 | |
| y25 | 4.7385 | 5.4082 | 4.6937 | 4.401 | 4.4138 | |
| Response | y31 | 4.6849 | 5.2245 | 4.6982 | 4.5417 | 4.2184 |
| y32 | 4.5523 | 5.0357 | 4.5135 | 4.4167 | 4.2069 | |
| y33 | 4.7628 | 5.2857 | 4.7613 | 4.625 | 4.3276 | |
| y34 | 4.6939 | 5.2704 | 4.6441 | 4.5938 | 4.2184 | |
| y35 | 5.0077 | 5.5153 | 4.9009 | 4.8854 | 4.7069 | |
| y36 | 4.7321 | 5.301 | 4.7387 | 4.6146 | 4.2126 | |
| y37 | 4.9885 | 5.5255 | 4.9369 | 4.8073 | 4.6494 | |
| y38 | 4.7564 | 5.3214 | 4.6982 | 4.599 | 4.3678 | |
| Recovery | y42 | 4.6735 | 5.2908 | 4.6712 | 4.4531 | 4.2241 |
| y43 | 4.6594 | 5.2551 | 4.6622 | 4.5 | 4.1609 | |
| y44 | 4.4605 | 4.7653 | 4.5 | 4.3333 | 4.2069 | |
| y45 | 4.7156 | 5.3571 | 4.6802 | 4.5573 | 4.2126 |
Post-test of the variance analysis of local government environmental emergency management capability.
| Dependent Variable: Local Government Environmental Emergency Management Capability | |||||
|---|---|---|---|---|---|
| (I) Region | (J) Region | Mean Difference (I–J) | Standard Error | Sig. | |
| LSD | Eastern | Central | 0.58206 * | 0.14438 | 0.000 |
| Western | 0.78773 * | 0.14957 | 0.000 | ||
| Northeastern | 1.08184 * | 0.15366 | 0.000 | ||
| Central | Eastern | −0.58206 * | 0.14438 | 0.000 | |
| Western | 0.20566 | 0.14517 | 0.157 | ||
| Northeastern | 0.49977 * | 0.14939 | 0.001 | ||
| Western | Eastern | −0.78773 * | 0.14957 | 0.000 | |
| Central | −0.20566 | 0.14517 | 0.157 | ||
| Northeastern | 0.29411 | 0.15441 | 0.057 | ||
| Northeastern | Eastern | −1.08184 * | 0.15366 | 0.000 | |
| Central | −0.49977 * | 0.14939 | 0.001 | ||
| Western | −0.29411 | 0.15441 | 0.057 | ||
| Tamhane | Eastern | Central | 0.58206 * | 0.1212 | 0.000 |
| Western | 0.78773 * | 0.13907 | 0.000 | ||
| Northeastern | 1.08184 * | 0.17001 | 0.000 | ||
| Central | Eastern | −0.58206 * | 0.1212 | 0.000 | |
| Western | 0.20566 | 0.13604 | 0.571 | ||
| Northeastern | 0.49977 * | 0.16754 | 0.018 | ||
| Western | Eastern | −0.78773 * | 0.13907 | 0.000 | |
| Central | −0.20566 | 0.13604 | 0.571 | ||
| Northeastern | 0.29411 | 0.18089 | 0.486 | ||
| Northeastern | Eastern | −1.08184 * | 0.17001 | 0.000 | |
| Central | −0.49977 * | 0.16754 | 0.018 | ||
| Western | −0.29411 | 0.18089 | 0.486 | ||
Note: * The mean difference was significant at the 0.05 level.
Multivariate ANOVA model estimation.
| Value | F | DF1 | DF2 | Sig. | ||
|---|---|---|---|---|---|---|
| Intercept | Pillai’s Trace | 0.906 | 1881.893 a | 4 | 777 | 0.000 |
| Wilks’ Lambda | 0.094 | 1881.893 a | 4 | 777 | 0.000 | |
| Hotelling’s Trace | 9.688 | 1881.893 a | 4 | 777 | 0.000 | |
| Roy’s Largest Root | 9.688 | 1881.893 a | 4 | 777 | 0.000 | |
| Area | Pillai’s Trace | 0.115 | 7.79 | 12 | 2337 | 0.000 |
| Wilks’ Lambda | 0.885 | 8.089 | 12 | 2056.04 | 0.000 | |
| Hotelling’s Trace | 0.129 | 8.356 | 12 | 2327 | 0.000 | |
| Roy’s Largest Root | 0.125 | 24.336 b | 4 | 779 | 0.000 |
Note: a. Accurate statistics. b. This statistic is the upper limit of F, which produces a lower limit on significance level.
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1 School of Public Affairs, Zhejiang University, Hangzhou 310058, China;
2 School of Public Affairs, Zhejiang University, Hangzhou 310058, China;
3 School of Management, Guizhou University, Guiyang 550025, China; School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia;
4 School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia;