The impact of regional emergency logistics response capacity on sustainable economic growth in China

Abstract

To assess the impact of China’s regional emergency logistics response capabilities on economic growth sustainability, this study develops a set of comprehensive indicators. It applies the entropy weight method combined with the TOPSIS method to evaluate these capabilities across regions. Based on panel data from 30 provinces in China from 2012 to 2021, this study employs panel models, panel quantile regression, and panel threshold models to analyze the impact of emergency logistics response capability on sustainable economic growth. By measuring the regional emergency logistics response capability and examining its effects on the sustainability of economic growth, this research provides foreign investors with a clearer understanding of the ability of different regions in China to handle unexpected public incidents. This, in turn, helps reduce investment uncertainty, mitigate certain risks, and offers valuable insights for enhancing the efficiency of international investments in China. Key findings are: (1) Since 2018, the establishment of the Ministry of Emergency Management has significantly improved China’s regional emergency logistics capabilities, with the eastern region outperforming the central and western regions. (2) Enhancing China’s emergency logistics response capability helps improve the sustainability level of economic growth. This reflects how China’s emergency management departments can mitigate the adverse impacts of public emergencies on economic growth by transforming social logistics into emergency logistics. (3) As emergency response capabilities have strengthened, their impact on economic sustainability initially decreased but later increased.

Introduction

In the twenty-first century, political, economic, and social challenges have intensified globally, with pronounced effects in developing nations. China, in particular, faces significant public safety concerns due to rapid industrialization and urbanization, alongside deteriorating atmospheric and geological conditions. Frequent public health incidents, natural disasters, and industrial accidents pose substantial threats to its social stability and economic growth. The “2021 Global Natural Disaster Assessment Report” notes that China experienced 21 severe natural disasters in 2021 alone, leading to direct economic losses of over 334 billion RMB and significantly affecting the lives of 107 million people.

As the world’s second-largest economy with diverse and complex geographical features, China encounters a variety of sudden public events. Its emergency logistics system, developed through extensive experience in responding to such incidents, offers valuable insights for comprehensive emergency preparedness. While much research has focused on immediate disaster responses and post-disaster reconstruction, studies examining the linkage between emergency logistics and economic growth remain limited. Emergency logistics are crucial for responding effectively to severe natural disasters, public health emergencies, and military conflicts. These operations prioritize rapid material, personnel, and financial support to maximize time efficiency and minimize disaster-related losses¹. In the context of frequent public emergencies, developing a robust emergency logistics system is essential for quickly delivering relief supplies and personnel, saving lives, and mitigating economic impacts. This system is not only critical for disaster relief and emergency management but also vital for ensuring sustainable economic growth.

During the 19th National Congress of the Communist Party of China, General Secretary Xi Jinping emphasized the need for enhanced emergency response capabilities to keep pace with economic development. This is crucial as China transitions from high-speed to high-quality growth. In addition, public emergencies pose various risks to international investments in China from other countries. Sudden public health events, such as the COVID-19 pandemic, have led to restrictions on cross-border personnel movement, supply chain disruptions, and delays or cancellations of project execution. Antràs et al.² emphasized that the rapid global spread of COVID-19 has significantly heightened uncertainty and increased risks across various markets, placing considerable financial pressure on governments in different regions. To mitigate these risks, investors need to develop flexible investment strategies. Enhancing emergency logistics response capability can help curb the escalation of public emergencies, strengthen supply chain resilience, and reduce risks associated with international investments. Therefore, understanding the impact of regional emergency logistics capabilities on economic sustainability can inform international direct investment decisions in Chinese regions. This research helps determine whether different areas can provide effective emergency support for necessary resources, guiding international investors in making informed regional choices, assessing direct investment risks accurately, and reducing international investment risks while improving investment efficiency.

The remainder of this paper is organized as follows: Section “Literature review” provides a literature review. Section “Theoretical framework” describes the theoretical framework regarding the impact of emergency logistics response capability on the sustainability of economic growth, including the underlying mechanisms. Section “Materials and methods” discusses the indicator system, research subjects, and research methods required for the empirical study. Section “Results” presents the empirical analysis based on panel data from 30 provinces in China from 2012 to 2021. Section “Discussion and managerial implications” covers the discussion and managerial implications, highlighting the innovation and practical significance of this research. Section “Conclusions and implications” concludes the paper, outlining its limitations and directions for future research.

Literature review

Previous research on emergency logistics related to the content of this paper has mainly focused on two areas: evaluating emergency logistics response capability and examining the impact of logistics capability on economic growth.

Evaluating emergency logistics response capacity

When assessing the effectiveness of emergency logistics response, scholars have developed various models. Tang et al.³ introduced a method based on the Analytic Hierarchy Process (AHP) and Hilbert space vector norms. Yang et al.⁴ noted that redundancy in emergency logistics indicators significantly impacts emergency plan assessments. They recommended using fuzzy cluster analysis for initial subjective evaluations, followed by grey relational analysis to refine the indicators and remove redundancies. Xu et al.⁵ created a system and model for assessing emergency logistics capabilities, using triangular fuzzy entropy and the Choquet integral to merge subjective judgment with objective evaluation. Ma et al.⁶ built a maturity model for emergency logistics response capabilities, which proved effective in determining maturity levels. Jiang et al.⁷ developed an evaluation system for emergency logistics during infectious disease outbreaks using a hybrid MADM model. Yang et al.⁸ established a grey comprehensive evaluation model for port emergency logistics systems using the Analytic Hierarchy Process and grey system theory. Yang et al.⁹ utilized a decision-making approach grounded in probabilistic linguistic terms to assess the capabilities of emergency logistics. Zhang¹⁰ analyzed public health emergency logistics capabilities during the COVID-19 pandemic with a BP neural network-based model. These studies highlight the importance of removing redundant indicators and combining subjective evaluations with objective data while addressing the complexity of emergency logistics environments with uncertainty management tools.

However, evaluation of emergency logistics response capabilities remains sparse. Although there has been notable progress, the reliance on subjective or mathematical methods has often undermined the scientific reliability of the results, especially regarding their universality and practicality. According to Ou et al.¹, emergency logistics shares its fundamental elements and societal functions with regular logistics, but requires much faster responses during public emergencies. Essentially, social logistics forms the basis of emergency logistics. Evaluating emergency logistics from the perspective of social logistics is crucial to enhance the models’ applicability and universality, a consideration often overlooked in existing studies.

The impact of logistics capacity on economic growth

Additionally, another group of scholars focuses on the impact of logistics capabilities on economic growth. For instance, Yan et al.¹¹ found significant regional variations in the degree of coupling and coordination between China’s economic growth and logistics development. Researchers like Saidi¹², Larson¹³, and Aden et al.¹⁴ have empirically demonstrated a positive effect of regional logistics capabilities on regional economic growth. In terms of sustainable economics, Chakamera et al.¹⁵ explored the relationship between logistics and sustainable economic growth in Africa, discovering that the “logistics capacity and quality” indicator produced a relatively high sustainability effect on economic growth. A review and synthesis of existing research reveal that many scholars have used various methods to investigate the impact of logistics capabilities on economic growth, confirming their promotional relationship. Nevertheless, amid the frequent occurrence of unexpected public events, current research has not adequately addressed how to effectively convert regular social logistics capabilities into those required for emergency response, nor has it examined the lasting economic impacts of these emergency logistics capabilities.

Extended research

Building on the insights from previous reviews, this paper extends the research in the following ways: (1) It integrates the components of emergency logistics to construct a detailed quantitative index system for emergency logistics response capabilities, utilizing the entropy weight TOPSIS method. This method, a decision-making analysis tool, evaluates the overall level of multiple options and is employed to assess emergency logistics response capabilities across Chinese regions. (2) The study analyzes the underlying mechanism of transforming regular social logistics into emergency logistics and the mechanism by which emergency logistics response capability affects the sustainability of economic growth. It further explores the potential link between these two mechanisms, clarifying the relationship between the transformation of social logistics into emergency logistics and economic growth. Based on this, panel regression models, panel quantile regression models, and panel threshold models are constructed to empirically examine the impact of emergency logistics response capability on the sustainability of economic growth, the trend of this impact, the relationship between public emergencies and emergency logistics response capability, and their effects on the sustainability of economic growth.

Theoretical framework

Emergency logistics refers to the process of efficiently planning, organizing, and controlling the supplies needed for sudden events by utilizing modern information and management technologies to integrate functions such as procurement, transportation, storage, loading and unloading, handling, packaging, distribution, and delivery, aiming to maximize time efficiency and minimize disaster losses¹⁶. Social logistics involves managing the flow of essential materials and related information to achieve time–space advantages, which ensures the normal functioning of society and enhances quality of life¹⁷. Scholars have found through their research that emergency logistics and social logistics share fundamental components such as fluid carriers, flow volume, flow direction, processes, and flow rate¹. They emphasize that during public emergencies, it is crucial to transform standard social logistics into emergency logistics to meet the demand for urgent supplies, making emergency logistics response capability vital in such situations¹⁸. This capability involves a broad set of skills critical to logistics operations, especially in terms of response time, speed, and the quality of transporting emergency supplies. It also encompasses the punctuality and reliability of emergency supply delivery. Understanding the adaptation of social logistics into emergency logistics operations and the challenges involved, such as adjusting logistics operations and maintaining coordination under pressure, is crucial for enhancing the robustness and effectiveness of emergency logistics systems. This deeper insight into the transformation process not only clarifies the practical aspects but also adds significant value for readers seeking a comprehensive understanding of emergency logistics capabilities.

Direct impact of emergency logistics response capacity on the sustainability of economic growth

In response to a public emergency, the emergency management department must repurpose social logistics resources for the delivery of emergency supplies. It can be achieved through economic, weak economic, and administrative orders. In this process, it is crucial for the emergency management departments to effectively identify the scale, trend, characteristics, and response level of public emergencies¹⁹. Only by doing so can the emergency logistics system be uniformly deployed, arranged, and coordinated, ultimately forming the emergency logistics response capacity to deal with public emergencies. It effectively ensures the sustainability of economic growth in addition to assisting in reducing the harm that public emergencies cause to the social economy. Therefore, the effectiveness of emergency logistics response capabilities directly affects the sustainability of social and economic growth. The impact of emergency logistics response capability on the sustainability of economic growth is mainly twofold. Suppose the government’s emergency management department cannot efficiently adapte routine social logistics into emergency logistics response capacity through the economy, the weak economy, and administrative orders. In that case, it will not be able to address the harm that public emergencies cause effectively. Suppose this harm cannot be alleviated or eliminated. In that case, it may lead to the sustained increase in the scale of public emergencies, which in turn results in a more significant impact on the social economy and ultimately has an inhibitory effect on the sustainability of economic growth. Conversely, an effective emergency logistics response capability promotes the sustainability of economic growth. The specific mechanism is shown in Fig. 1.

Fig. 1

Direct impact of emergency logistics response capacity on the sustainability of economic growth.

Trends in the influence of emergency logistics response capacity on the sustainability of economic growth

The service functions of emergency logistics and social logistics are consistent. Emergency logistics is a transformation of social logistics during public emergencies. Social logistics acts as a sustainable artery for social and economic growth, effectively promoting its continuity. When public emergencies occur, the emergency management department shifts to an emergency logistics system, improving emergency logistics response capabilities. However, this transformation may temporarily reduce the ability of social logistics to serve the social economy. As emergency logistics response capacity improves, the promotion effect on the sustainability of economic growth may initially decline due to the weak economy of emergency logistics.

Nonetheless, through continuous improvement in emergency logistics response capacity, the adverse effects of public emergencies are inhibited, gradually restoring orderly production, living conditions and promoting the sustainability of economic growth. After eliminating public emergencies and transforming emergency logistics response capabilities into social logistics, the ability to serve the normal economic development of society will continue to increase, jointly promoting the sustainability of economic growth. Therefore, the driving effect of emergency logistics response capacity on sustainable economic growth first weakens and then strengthens as the capacity improves. The specific mechanism is illustrated in Fig. 2.

Fig. 2

Direct impact of emergency logistics response capacity on the sustainability of economic growth.

Due to the apparent regional imbalance in the advancement of China’s logistics sector, the quality and efficiency of social logistics development in different regions are heterogeneous. Therefore, the social logistics resources the emergency management department mobilizes through economic, weak economic, and administrative orders in emergencies are limited. On the one hand, when a region’s social logistics development is highly efficient, the emergency management department can effectively transition social logistics into emergency logistics response capability during public emergencies. If the region has sufficient social logistics resources, there will still be abundant resources even after the transformation. This surplus ensures the sustainability of economic growth in the area with high development efficiency. Although emergency logistics has a weak economy, it does not affect the sustainability of social and economic growth. Under emergency logistics response capacity, it can effectively reduce or eliminate the negative effect of public emergencies on sustainable socio-economic growth. Therefore, under the influence of the above internal mechanism, after social logistics is transformed into the emergency logistics system, the level of emergency logistics response capacity will continue to improve, thereby promoting the sustainability of economic growth. It shows that the impact of emergency logistics response capacity on the sustainability of economic growth will continue to increase with the strengthening of emergency logistics response capacity.

On the other hand, in the event of a public emergency, the social logistics resources that the government emergency management department can mobilize are limited. Transforming some ordinary social logistics resources into the emergency logistics system can promote the sustainability of economic growth due to the high efficiency of social logistics operations in the remaining part. Improving emergency logistics response capacity can gradually eliminate the adverse effects of public emergencies and promote the continuous development of social and economic order in a normal direction. It is reflected in the way that enhanced emergency logistics response capacity contributes to sustained economic growth. However, there is a risk of excessively diverting social logistics resources into the emergency logistics system when dealing with public emergencies. This behaviour will continue to squeeze the remaining capacity of social logistics to serve society, and the strengthening of emergency logistics response capacity will reduce the driving effect on the sustainability of economic growth. Therefore, under the impact of this mechanism, the impact on the sustainability of economic growth is first rising and then falling with the continuous improvement of emergency logistics response capacity.

The relationship between public emergencies and emergency logistics response capacity and its impact on the sustainability of economic growth

Combined with the above mechanism analysis, public emergencies have the potential to harm the stability of sustainable economic growth. However, the emergency logistics response can effectively mitigate or eliminate the adverse effects of such emergencies. It will demonstrate the relationship between these factors and discuss how they can impact the sustainability of economic growth.

Firstly, emergency logistics response capacity has a moderating effect on public emergencies. It can effectively reduce or eliminate their adverse impact on the sustainability of economic growth and maintain the stability of social and economic systems. Therefore, as the emergency logistics response capacity continues to improve, the inhibitory effect of public emergencies on the sustainability of economic growth will continue to decrease or even disappear.

Secondly, if the existing emergency logistics response capability cannot match the scale, response level, and trend of public emergencies and falls short of the demand for responding to them, public emergencies will hinder the sustainability of economic growth. Despite the improvement in emergency logistics response capability, the expanding scale of public emergencies cannot be effectively controlled, leading to an increasing inhibitory effect on the sustainability of economic growth.

Thirdly, when a public emergency occurs, the emergency management department organizes and coordinates social logistics through economic, weak economic, and administrative orders. This could enhance emergency logistics response capacity and effectively mitigate the impact of public emergencies on social and economic sustainability. Additionally, it could incentivize the government emergency management departments to allocate more resources toward improving this capacity to better anticipate future risks, for example, by continuously improving the logistics infrastructure and optimizing the emergency logistics material transportation support networks and information systems. After countering public emergencies, the capacity of emergency logistics response can be quickly transformed into social logistics services under the guidance of emergency management agencies. Enhancing logistics infrastructure, optimizing emergency logistics and transportation support networks, and improving information systems will be crucial drivers of economic growth. As a result, public emergencies will play an active role in promoting the sustainability of economic growth. This role will become more significant as the capacity for emergency logistics response continues to improve.

However, it is essential to avoid over-investing in a region’s emergency logistics response capacity. Otherwise, regular social logistics may be disregarded or incorporated into emergency logistics without effectively minimizing or eliminating the harm caused by public emergencies. Therefore, optimizing logistics infrastructure, emergency logistics, transportation support networks, and information systems should not compromise social and economic growth needs. In an environment with excessive investment in emergency logistics response capacity, public emergencies will only have an uplifting effect on the sustainability of economic growth when emergency logistics response capacity is below a certain threshold.

Materials and methods

Components of the emergency logistics response capability

In empirical studies on logistics capacity, numerous scholars have reached a certain consensus on the measurement metrics of logistics carrier carrying capacity, logistics flow, and flow rate capacity. Based on the above elements, this paper draws on the analysis and evaluation index selection of emergency logistics capability by Xi Menghao²⁰, Identifying components of emergency logistics response capability through indicators such as emergency logistics carrier capacity, flow speed capacity, and flow efficiency capacity. Meanwhile, if indicator measurement is not implemented based on the current state of regional emergency logistics response capability, it will hinder the improvement of such capability. Therefore, it is necessary to further consider the practical factors amid public emergencies. This study posits that under the effect of public emergencies, the ability to safeguard public medical resources is the responsibility of governments at all levels. Drawing on Li²¹, relevant indicators of social emergency support capacity are introduced as components of emergency logistics response capability. Additionally, certain researchers argue that regional informatization capability is a key factor influencing the speed of shifts in emergency logistics response capability²². Hence, regional informatization capability is additionally incorporated as an element of emergency logistics response capability.

In the selection of sample data, Given the absence or differing statistical methodologies of sample data from Hong Kong, Macau, Taiwan, and Tibet, this study is confined to the thirty provinces of Mainland China. The year 2012 was selected as the starting point as it marks the release of the first policy on emergency industry development by the General Office of the State Council of China, titled “Opinions on Accelerating the Development of the Emergency Industry.” Additionally, in 2018, China established the Ministry of Emergency Management, and various provinces also set up corresponding emergency management departments. Regarding the selection of the analysis period, the sample observation period is set from 2012 to 2021, as obtaining data beyond 2021 is relatively challenging. Based on previous research experience, a 10-year span is considered a valuable observation window for exploring the cyclical changes of a research subject, as it allows for the observation of long-term trends and cyclical variations while ensuring data relevance and contemporaneity. The data come from the 'China Statistical Yearbook’ and the National Bureau of Statistics website. Data voids are handled through interpolation or analogy, while some indicators are derived from the original data. The detailed index system is presented in Table 1.

Table 1 Constituents and index system of the emergency logistics response capability.

Method of measuring emergency logistics response capability

The entropy-weighted TOPSIS method (Hereafter referred to as EWTOPSIS) is a synthesis of the entropy-weighted method and the TOPSIS method. It is a goal-oriented decision analysis method for multi-scheme, comprehensive-level evaluation. The advantage of the entropy-weighted method is that it standardizes the indicators and determines the weights objectively based on the degree of variation, thus avoiding interference from human factors²³. The merit of using the TOPSIS method is its ability to quantify rankings by calculating the relative distances of an evaluation object from both the ideal and negative-ideal points, enabling a comprehensive assessment that accounts for both best and worst-case scenarios²⁴. This paper uses EWTOPSIS to assess the emergency logistics response capacity from 2012 to 2021. Based on the normalization of each measurement index, the core idea is that it uses the entropy-weighted method to determine the weight for each measurement index. Then, it applies the TOPSIS method to quantify each province’s emergency logistics response capacity. The specific calculation steps are as follows:

• 1. Standardized processing of raw data

  To facilitate the comparison of indicators across different provinces and years, this study employed the Min–Max Scaling method to normalize the data, removing its dimensionality.

  $$x^{\prime}_{\theta ij} = \frac{{x_{\theta ij} - \min \{ x_{\theta 1j} , \ldots ,x_{\theta nj} \} }}{{\max \{ x_{\theta 1j} , \ldots ,x_{\theta nj} \} - \min \{ x_{\theta 1j} , \ldots ,x_{\theta nj} \} }}$$

  (1)

  Among these, there are $r$ years, $n$ provinces, and $m$ indicators, $x_{\theta ij}$ represents the value of the $j$ indicator for the $i$ province in the $\theta$ year. Here, $\theta = 1,2, \ldots ,r;i = 1,2, \ldots ,n;j = 1,2 \ldots ,m$. $x_{\theta ij}$ represents the original value (initialization value) of the respective indicator. $x_{\max }$ and $x_{\min }$ respectively represent the maximum and minimum values within the group of that particular indicator. $x^{\prime}_{\theta ij}$ represents the standardized value of a specific indicator after normalization.

• 2. Calculate the weights

  $$p_{\theta ij} = \frac{{x^{\prime}_{\theta ij} }}{{\sum\nolimits_{\theta = 1}^{r} {\sum\nolimits_{i = 1}^{n} {x^{\prime}_{\theta ij} } } }}$$

  (2)
  $$e_{j} = - \frac{{\sum\nolimits_{\theta = 1}^{r} {\sum\nolimits_{i = 1}^{n} {(p_{\theta ij} *\ln p_{\theta ij} )} } }}{\ln rn}$$

  (3)
  $$w_{j} = \frac{{1 - e_{j} }}{{\sum\nolimits_{j = 1}^{n} {(1 - e_{j} )} }}$$

  (4)

  where $p_{\theta ij}$ represents the feature weight value of the $j$ indicator for the $i$ provincial administrative region. $e_{j}$ denotes the information entropy of the $j$ indicator. $w_{j}$ signifies the weight of the $j$ indicator, $w_{j} \in \left[ {0,1} \right]$ and $\sum\limits_{j = 1}^{n} {w_{j} } = 1$.

• 3. Create the weighted matrix $Z$

  $$Z = \left( {z_{\theta ij} } \right)_{r \times m \times n} ,\;z_{\theta ij} = w_{j} \times x^{\prime}_{\theta ij} \left( {\theta = 1,2, \ldots ,r;i = 1,2, \ldots ,n;j = 1,2, \ldots ,m} \right)$$

  (5)

  where $z_{\theta ij}$ represents the weighted decision score. $Z$ is the weighted decision matrix composed of all weighted decision scores.

• 4. Calculate Euclidean distance

  $$D_{\theta i}^{ + } = \sqrt {\sum\nolimits_{j = 1}^{n} {(d_{j}^{ + } - z_{\theta ij} )^{2} } } \quad d_{j}^{ + } = \max (z_{\theta ij} )$$

  (6)
  $${\text{D}}_{\theta i}^{ - } = \sqrt {\sum\nolimits_{j = 1}^{n} {({\text{d}}_{j}^{ - } - z_{\theta ij} )^{2} } } \quad d_{j}^{ - } = \min (z_{\theta ij} )$$

  (7)

  Among these, $d_{j}^{ + }$ and $d_{j}^{ - }$ respectively represent the positive and negative ideal solutions. The positive ideal solution represents the region with the most desirable development in emergency logistics response capacity among all provinces, while the negative ideal solution represents the region with the least desirable development in emergency logistics response capacity. $D_{\theta i}^{ + }$ and $D_{\theta i}^{ - }$ represent the Euclidean distances between the actual level of emergency logistics response capacity for each province and the positive and negative ideal solutions, respectively.

• 5. Calculate the emergency logistics response capacity $C_{\theta i}$

  $$C_{\theta i} = \frac{{D_{\theta i}^{ - } }}{{D_{\theta i}^{ + } + D_{\theta i}^{ - } }}$$

  (8)

  Here, $C_{\theta i} \in \left[ {0,1} \right]$. The closer the value of $C_{\theta i}$-value is to 1, the more enhanced the emergency logistics response capacity within the region. Conversely, the closer the value of $C_{\theta i}$-value is to 0, the lower the emergency logistics response capacity in the region.

Indicator selection and empirical model construction

Indicators selection

Sustainability of economic growth (lnGDP). In line with the objective of this study, we consider the sustainability of economic growth as the dependent variable. To assess the sustainability of economic growth, this study adopts the approach proposed by Wang et al.²⁵, using the growth rate of gross domestic product (GDP) as an alternative indicator. GDP provides a clear quantitative standard for measuring the overall size and growth rate of an economy. According to previous research, it is widely used by scholars both domestically and internationally as an indicator for economic growth. This choice is derived from the key insights it offers and its extensive practical applications. Therefore, this study selects the GDP growth rate in each province from 2012 to 2021 as the alternative indicator, with data sourced from the China Statistical Yearbook.

Emergency logistics response capacity (lnlogistics). This study measures the value of emergency logistics response capacity using EWTOPSIS from 2012 to 2021. Based on this paper’s research objective and mechanism, the emergency logistics response capacity (lnlogistics) is considered the core explanatory variable. For specific measurement methods, see Sections “Components of the emergency logistics response capability” and “Method of measuring emergency logistics response capability” above.

Additionally, this paper examines the outcomes associated with emergency logistics response capability on the sustainability of economic growth. In addition to emergency logistics response capability, numerous factors influence the sustainability of economic growth, including the level of government expenditure, labor input, scale of fixed asset investment, and economic openness. So, this paper selects the control variables mentioned to be included in the analysis model. The specific control variables are selected as follows:

• The scale of investment in fixed assets (lnK). The classical theory of the sustainability of economic growth asserts that the accumulation of material capital is a crucial element impacting this sustainability. Being an influential variable for economic development, investment in fixed assets undeniably acts as a significant driving force²⁶. Therefore, in this study, the total investment in fixed assets serves as a representative measure of the capital investment in each province.

• Labor input scale (lnL). According to Lucas model, the labor force is an essential economic variable in output and the most active production factor, which is an important factor affecting the sustainability of economic growth²⁷. Therefore, this paper takes the total employment population of urban units, private enterprises, and individual enterprises to represent the labor force input situation.

• Level of government expenditure (lnGov). The local government’s economic policy is an essential component that affects long-term economic growth. Government expenditure is one of the important manifestations of the government’s efforts to stimulate the sustainability of economic growt²⁸. This paper takes provincial public financial spending proportion in the regional GDP as the alternative variable for the level of government expenditure.

• Economic openness (lnOpen). The economic development of a region depends not only on its sustainable growth potential but also on its openness to the outside world²⁹. This paper expresses the openness to the outside world as the proportion of total export–import volume to regional GDP.

• Public emergency scale (lnaccident). The purpose of this paper is to investigate the relationship between the scale of sudden public emergencies and emergency logistics response capacity and their impact on the sustainability of economic growth. This paper uses the total number of fires, geological disasters, and sudden environmental events from 2012 to 2021 as a proxy variable for the scale of sudden public emergencies.

The above control variables are sourced from each Province’s Statistical Yearbook or Economic Yearbook (autonomous region and centrally administered municipality) in China, with a sample observation period from 2012 to 2021. The specific variable definitions and explanations are shown in Table 2.

Table 2 Definitions and descriptions of variables.

Empirical model construction

Based on the mechanism described above, which focuses on the sustainable impact of economic growth on the response capacity of emergency logistics, this study selects the sustainability of economic growth as the dependent variable. It aims to explore the relationship between sustainable economic growth and the response capacity of emergency logistics. To achieve this, a panel regression model is constructed as follows:

$$lnGDP_{it} = a_{i} + b_{1} lnlogistics_{it} + b_{2} lnK_{it} + b_{3} lnL_{it} + b_{4} \ln Gov_{it} + b_{5} \ln Open_{it} + \delta_{it}$$

(9)

Furthermore, the panel quantile regression model, which aims to analyze the mechanism of emergency logistics response capacity’s impact on the sustainability of economic growth, is constructed as follows:

$$lnGDP = \alpha_{1q} + b_{2q} lnlogistics_{tq} + \delta_{t}$$

(10)

In addition, this paper explores the relationship between public emergencies, emergency logistics response capability, and the sustainability of economic growth. By introducing a threshold variable $lnlogistics$, the paper aims to adjust the nonlinear impact of the scale of public emergencies ($lnaccident$) on the sustainability of economic growth. Therefore, we construct a panel threshold model as follows:

$$\begin{aligned} lnGDP_{it} = & \alpha_{i} + \beta_{11} lnaccident_{it} f(lnlogistics_{it} \le x_{1} ) + \beta_{12} lnaccident_{it} f(x_{1} < lnlogistics_{it} \le x_{2} ) \\ & \quad + \beta_{1,n} lnaccident_{it} f(x_{n - 1} < lnlogistics_{it} \le x_{n} ) + \beta_{{1,n{ + }1}} lnaccident_{it} f(x_{n} < lnlogistics_{it} ){ + }\lambda x_{it} + \sigma_{it} \\ \end{aligned}$$

(11)

In the above model,$lnGDP_{it}$ represents the sustainability of economic growth level. $lnlogistics_{it}$ represents the emergency logistics response capacity. $lnK_{it}$ represents the scale of fixed asset investment. $lnL_{it}$ represents the scale of labour input; $\ln Gov_{it}$ represents the level of government expenditure. $\ln Open_{it}$ represents the economic openness; $lnlogistics_{tq}$ represents the emergency logistics response capacity at different quartiles (0.25, 0.5, and 0.75 quartiles are selected in this study). $lnaccident_{it}$ represents the scale of public emergencies.

In model 11, $x_{1}$,$x_{2}$……$x_{{\text{n - 1}}}$, and $x_{n}$ are thresholds and there are n + 1 threshold intervals. $\beta_{11}$,$\beta_{12}$, ……,$\beta_{1n}$,$\beta_{1n + 1}$ are the regression coefficients of the variables at different threshold intervals. $f( \cdot )$ is the indicator function which takes the value 1 if the threshold variable conforms to the value conditions defined by the formula, and 0 otherwise. Where $x_{it}$ is the other control variables in the model. $x_{it}$ is the regression coefficient of the control variables (lnL, lnK, lnGov, lnOpen).

Data descriptive statistics

This paper analyzed relevant data from 30 provinces (municipalities and districts) between 2012 and 2021. Stata 17.0 was employed to conduct descriptive statistical analysis on each variable. The data demonstrate significant regional disparities in emergency logistics response capacity among Chinese provinces (municipalities and regions). Table 3 displays the descriptive statistics for the panel data sample comprising 270 observations.

Table 3 Variable descriptive statistics values.

The mean of regional emergency logistics response capacity (lnlogistics) is 0.188, with a standard deviation of 0.094. The minimum value is 0.044, and the maximum value is 0.595. The sample data reveal significant disparities in emergency logistics response capacity across different regions, with some regions exhibiting much higher capabilities than others. This suggests an imbalance in regional emergency logistics response capacity. Moreover, regarding sudden public emergency scale (lnaccident), the average value is 8.645, suggesting a relatively high scale of such events. The standard deviation of 0.094 indicates that the scale does not vary significantly. However, there are regions where the scale reaches as high as 10.243, while in other regions it reaches as low as 6.564. This reveals significant differences among different areas. As for the selected control variables, the standard deviations of fixed asset investment (lnK), labor input (lnL), government expenditure level (lnGov), and economic openness (lnOpen) are 0.792, 0.827, 5.130, 0.104, and 0.951, respectively. This suggests some variation in the data for each variable in the sample. The above data provide a reference for selecting the following empirical method.

Results

Measurement results and evolutionary trends

Using the above index system, this study employed Stata software and EWTOPSIS to evaluate the emergency logistics response capabilities of 30 provinces (including municipalities and autonomous regions) from 2012 to 2020. To comprehensively understand the development trajectory of regional emergency logistics response capability, this study adopts the regional divisions of China as delineated by the National Development and Reform Commission, categorizing China’s 30 provincial-level regions into three major economic regions: Central, Eastern, and Western. It analyzed the results of the measurement for each of these regions.

Based on the calculations in Table 4, national emergency logistics response capability exhibited the following characteristics: Firstly, each Chinese province’s emergency logistics response capacity has significantly improved from 2012 to 2021. Secondly, Qinghai was the province with the weakest emergency logistics response capacity during the observed period, with an average value of only 0.0552. Guangdong had the highest emergency logistics response capacity, with an average value of 0.4696. It indicated a significant gap in emergency logistics response capacity among Chinese provinces. Finally, a numerical analysis of the emergency logistics response capacity showed that the strong-capacity provinces were mainly concentrated in the eastern region. On the contrary, the provinces in the western region exhibited relatively weak capacity. It indicated a clear regional imbalance in National emergency logistics response capability.

Table 4 Calculation results of emergency logistics response capacity in China’s provinces.

Furthermore, this study analyzed the changing trend of emergency logistics response capacity in different regions of China, as shown in Table 5 and Fig. 3. Nationally, China’s regional emergency logistics response capacity has markedly improved over the observation period, with the average rising from 0.1577 in 2012 to 0.2370 in 2021, a 50.29% increase. The most significant growth occurred between 2017 and 2018. One possible reason for this was the country’s establishment of an emergency management department in 2018, which led to the release of corresponding planning and the full deployment of emergency logistics as a critical construction project. Therefore, China’s emergency logistics response capability has rapidly improved.

Table 5 Calculation results of emergency logistics response Capacity for Different Regions in China.

Fig. 3

Dynamic evolution of China’s emergency logistics response capacity from 2012 to 2021.

From a regional perspective, the emergency logistics response capacity in China’s central, eastern, and western regions has demonstrated a steady upward trend. The emergency logistics response capacity of the eastern region was greater than that of the central and western regions. The emergency logistics response capacity of the central and eastern regions was higher than the national average, while the western region’s capacity fell below the national average. In addition, in terms of the improvement of emergency logistics response capacity, the average growth rate in the eastern region was as high as 6.12%. In comparison, the western region increased by 4.18% and the central region increased by 3.41%. The emergency logistics response capability improvement exhibited significant regional heterogeneity.

The following factors mainly cause interregional disparities in China’s emergency logistics response capacity: Firstly, China possesses abundant land resources, which include diverse geographical and climatic conditions. These conditions lead to significant differences in natural conditions and the scale, type, and severity of public emergencies in the eastern, central, and western regions. For example, regarding the scale of public emergencies in China, the eastern region experiences more severe incidents than the central region. In contrast, the central region faces more serious incidents than the western region. These differences in the scale of emergencies result in uneven experiences and effectiveness in responding to emergencies, thus affecting the regional emergency logistics capacity.

Secondly, the lack of a comprehensive sharing system between relevant departments hinders the establishment of a cross-regional data-sharing platform for emergency information. This restriction temporarily inhibits the full utilization of the advantages provided by concerned departments such as emergency management, the State Bureau of Grain Reserve, and other relevant departments. Therefore, the inadequate cross-regional allocation mechanism for emergency supplies impedes the formation of joint forces to enhance the coordination of cross-regional emergency logistics response capacities. Thirdly, the differences in logistics infrastructure in various regions of China lead to uneven levels of accessibility for emergency logistics transportation routes, resulting in disparities in emergency logistics response capabilities across different areas.

Lastly, there are disparities in early warning support capabilities among governments in different regions, including the availability and quality of medical resources, the scale of the Centers for Disease Control and Prevention (CDC), and the financial investments in local emergency logistics. As a result, these factors collectively contribute to the difference in emergency logistics response capacity among the regions in China.

Estimation results

Test of the direct impact of emergency logistics response capability on the sustainability of economic growth

This study employed a panel data model (9) to conduct regression analyzes on the overall sample of China as well as samples from the central, eastern, and western regions. The regression results are presented in Table 6. Based on the national sample, the regression coefficient of lnlogistics is positive and statistically significant. This indicates that China’s emergency logistics response capability can effectively enhance the sustainability of economic growth. It also reflects that national emergency management departments can efficiently convert ordinary social logistics into emergency logistics during sudden public emergencies. As a result, it can effectively reduce or eliminate the damage to the sustainability of economic growth caused by sudden public emergencies. Moreover, it effectively prevents further escalation of sudden public emergencies, demonstrating the positive impact of improved emergency logistics response capabilities on the sustainability of economic growth.

Table 6 Effect of emergency logistics response capacity on the sustainability of economic growth.

When analyzing the results by region, the regression coefficients of lnlogistics in China’s eastern, central, and western regions are positive and statistically significant. Moreover, the coefficient is higher in the eastern region than in the central region, and the coefficient is higher in the central region than in the western part. This indicates that emergency logistics response capacity in various regions of China can effectively address sudden public events and promote the sustainability of economic growth. The promotion effect of emergency logistics response capabilities in the eastern region is more significant than that in the central region, while it is weakest in the western region. This indirectly reflects the higher level of emergency management in the eastern region, surpassing the central region, which efficiently transforms ordinary social logistics into emergency logistics. Conversely, the western region exhibits the weakest level of emergency management in this regard. Additionally, all control variables (lnK, lnL, lnGov, and lnOpen) are significant considering the national sample and samples from the eastern, central, and western regions.

Trend test of the impact of emergency logistics response capacity on the sustainability of economic growth

This paper applied the panel quantile regression model (10) to conduct quantile regressions on the overall sample of China and samples from the eastern, central, and western regions. The results of the regression are presented in Table 7. As shown in Fig. 4, when examining the national sample, the coefficients of lnlogistics at the 0.25, 0.5, and 0.75 quantiles are all significantly positive. Furthermore, the coefficient decreases from 1.5319 to 1.450 and then increases to 1.462, indicating a decreasing and then increasing trend in the positive promotion effect of emergency logistics response capability on the sustainability of economic growth. This suggests that the emergency management departments can effectively enhance the emergency logistics response capability by transforming ordinary social logistics into the emergency logistics system when sudden public events occur. However, when emergency logistics are activated, it may temporarily reduce society’s capacity to provide standard logistics services due to its limited economic nature. This could lead to a temporary reduction in the promotion effect of emergency logistics response capabilities on the sustainability of economic growth. When emergency logistics response capacity effectively addresses sudden public events, it can reduce or even eliminate the damage caused by such events to the sustainability of economic growth and social stability, thus ensuring the continuous stability of sustainable socio-economic growth. Therefore, after addressing sudden public events, national emergency management departments can effectively shift the emergency logistics system back into the regular social logistics system, thereby promoting the sustainability of economic growth. From the national regression results above, the regression coefficients of lnlogistics at all quantiles are positive and significant, consistent with the conclusions in model (9), indicating the robustness of the regression results.

Table 7 Changing trend of the effects of China’s regional emergency logistics response capacity on the sustainability of economic growth.

Fig. 4

The trend in the impact of lnlogistics on the sustainability of economic growth in national sample.

When observing the results by region, the regression coefficients of lnlogistics at the 0.25, 0.5, and 0.75 quantile points in China’s central, and eastern regions are significant and positive. However, there are differences in the changing trends across the three regions. In the eastern regions, the estimated coefficients of lnlogistics continuously increase from 1.303 to 1.5049 at the quantile points of 0.25, 0.5, and 0.75. This overall trend reveals that the improvement of emergency logistics response capabilities has an increasingly significant effect on the sustainability of economic growth, as shown in Fig. 5. Regarding the central region, the estimated coefficients of lnlogistics at the quantile points of 0.25, 0.5, and 0.75 are 0.7295, 0.92556, and 1.0004, respectively. These values indicate a continuous contribution to the sustainability of economic growth as emergency logistics response capabilities improve, as depicted in Fig. 6. In contrast, the western region exhibits a different pattern. The coefficient estimates of lnlogistics at the quantile points of 0.25, 0.5, and 0.75 are 1.745, 1.811, and 1.532, respectively. This suggests that as the emergency logistics response capacity continues to improve in the western region, it first enhances the sustainability of economic growth and then diminishes, resulting in the pattern shown in Fig. 7. From the regional sample regression results above, the regression coefficients of lnlogistics at all quantiles in the central, eastern, and western regions are positive and significant, consistent with the conclusions in model (9), indicating the robustness of the regression results.

Fig. 5

The trend in the impact of lnlogistics on the sustainability of economic growth in eastern region.

Fig. 6

The trend in the impact of lnlogistics on the sustainability of economic growth in the central region.

Fig. 7

The trend in the impact of lnlogistics on the sustainability of economic growth in the western region.

Based on the regression results mentioned above, the emergency logistics response capabilities in the central and eastern regions of China promote the sustainability of economic growth. Furthermore, the trend of a continuous increase in the effect of emergency logistics response capabilities on the sustainability of economic growth is higher in the eastern region than in the central region. This suggests that eastern China has more abundant social logistics resources than central China. In addition, after converting general social logistics into emergency logistics, the eastern region’s emergency management department still has a relatively abundant residue of social logistics services that are more efficient in serving the social economy. Thus, this persistent advantage contributes to a sustained higher promotion effect on the sustainability of economic growth in the eastern region relative to the central region. On the contrary, the western region has a relatively higher efficiency in providing ordinary social logistics services. However, this behaviour will steadily reduce the remaining capacity of general social logistics services, leading to an initial increase and then a decrease in the promotion effect on the sustainability of economic growth.

Test of the relationship between public emergencies and emergency logistics response capacity and its impact on the sustainability of economic growth

Based on Model 11, this paper regressed the research samples using the panel threshold model. Firstly, this paper analyzed the presence of threshold effects in this model. The number of thresholds was determined by calculating the corresponding probability of the F-statistic using the self-sampling method. Based on this analysis, the form of the panel threshold model was determined. The F-statistic value and the corresponding probability value are shown in Table 8.

Table 8 Test results of threshold number.

This study used emergency logistics response capability as the threshold variable. The F-statistic analysis was carried out on a national sample. The results indicate that the F statistic of a single threshold rejects the null hypothesis of ‘no threshold’ and supports the alternative hypothesis of 'only one threshold’ at the 1% significance level. This confirms the existence of a single threshold for emergency logistics response capability, as shown in Fig. 8.

Fig. 8

Single threshold LR test for national sample.

Furthermore, a threshold test was conducted to evaluate the emergency response capacity in different regions. The results indicate that the F statistics in the eastern and central regions confirm the existence of double thresholds at the 1% significance level. In contrast, no threshold is found in the western region. Based on the analysis, the threshold values for emergency response capacity are determined for the national sample as well as the eastern and central regions. The single threshold value within the 95% confidence interval for the national sample is -1.4825. In the eastern region, the two thresholds are − 1.539 and − 1.288, respectively. In the central region, the two thresholds are 1.76 and − 1.435. These findings are presented in Table 9. Additionally, a likelihood test is conducted to further verify the existence of threshold variables in the whole country, the western region, and the eastern region. The test results are shown in Figs. 9 and 10.

Table 9 Results of threshold value assessment.

Fig. 9

Eastern sample threshold LR test. (a) Shows the single threshold of emergency logistics response capability in the eastern region. (b) Shows the dual threshold of emergency logistics response capability in the eastern region.

Fig. 10

Central sample threshold LR test. (a) Shows the single threshold of emergency logistics response capability in the central region. (b) Shows the dual threshold of emergency logistics response capability in the central region.

After estimating the threshold values r1 and r2, this article analyzed the panel model. The regression results in Table 10 show that, nationally, emergency logistics response capabilities positively impact economic growth sustainability when below − 1.4825. This suggests that emergency management departments may have over-invested in resources to enhance these capabilities.

Table 10 Threshold effects of emergency logistics response capacity.

According to regression results from the samples in the eastern region, the scale of public emergencies has a negative impact on the sustainability of economic growth when the emergency logistics response capacity falls below − 1.539. Furthermore, when the emergency logistics response capacity ranges from − 1.539 to − 1.288, this negative impact still exists but decreases. These findings suggest that upgrading emergency logistics response capabilities across eastern China can effectively mitigate the adverse effects of public emergencies on economic sustainability. As emergency logistics response capacity improves, the inhibitory effect of public emergencies on economic growth continues to decrease.

From the regression results of the sample in central China, we find that when the emergency logistics response capacity is lower than − 1.76, it has a negative impact on the sustainability of economic growth. However, this negative impact gradually decreases when the emergency logistics response capacity ranges between − 1.76 and − 1.435. This suggests that improving the emergency logistics response capacity in the central region can reduce the negative impact of public emergencies on economic growth. Furthermore, compared with the eastern region, the central region’s improvements in emergency logistics response capacity have resulted in a greater reduction of this negative impact on economic growth.

Discussion and managerial implications

Discussion

This paper constructed a measurement index for emergency logistics response capacity based on the concept and connotation of emergency logistics. It explored its impact on the sustainability of economic growth. The paper expanded the existing literature by addressing the shortcomings of subjective assessment methods used in domestic and foreign research on emergency logistics evaluation. Furthermore, the paper emphasized the consistency of constituent elements and social functions between emergency and ordinary logistics. The main distinction lies in the higher reaction time and speed requirements throughout emergency logistics activities. While emergency logistics is a part of social logistics in times of no public emergencies, it needs to transform into emergency logistics during public emergencies, highlighting the essential nature of social logistics capability in emergency logistics response. Unfortunately, this aspect has yet to be considered in existing research. Therefore, the study constructed a multidimensional and comprehensive index system for encompassing emergency logistics carrier capacity, emergency logistics response capacity, emergency logistics flow response capacity, emergency logistics flow velocity response capacity, emergency logistics flow efficiency response capacity, regional information response capability, and government warning assurance capacity. Unlike previous research, this index system effectively captures the connotations and features of emergency logistics response capability, enabling a more comprehensive evaluation. The study further utilized EWTOPSIS to evaluate the emergency logistics response capacities of different regions in China, enhancing the scientificity and credibility of the evaluation results. The study has revealed that national overall emergency logistics response capability showed a rising trend during the sample period. From a regional perspective, the differences between different regions indicate an imbalanced regional distribution. Specifically, the eastern region has the highest emergency logistics capabilities, followed by the central region and the western region. The emergency logistics response capabilities in the eastern and central regions exceed the national average, while those of the western region are lower than the national average.

In addition, numerous scholars have examined the impact of logistics capacity on economic growth using various methods and proven its positive influence. However, this issue has not received much attention in converting social logistics to emergency logistics in the context of frequent public emergencies. This conversion is crucial for reducing or eliminating the harm caused by public emergencies to economic growth and ensuring long-term, sustainable economic development. Therefore, this study aims to investigate the impact of regional emergency logistics response capacity on China’s economic growth sustainability. This study explored the influence of emergency logistics response capability on the sustainability of economic growth and analyzed its regional impact trend. This study also investigated the association between public emergencies, emergency logistics response capability, and economic growth sustainability. To empirically test the impact mechanism of emergency logistics response capability on sustainable economic growth during public emergencies, panel regression models, panel quantile regression models, and panel threshold models are employed. This study provides valuable theoretical insights by addressing the gap in existing research and considering the role of emergency logistics response capability in effectively ensuring sustainable economic growth despite frequent public emergencies.

Managerial implications

It is particularly relevant for China, given its complex and diverse topography and geomorphology, which result in various types of sudden public health events and natural disasters. China’s comprehensive and systematic experience managing public emergencies through emergency logistics can offer valuable lessons and insights. As a result, exploring the implications of China’s regional emergency logistics response capacity on the sustainability of economic growth can provide valuable insights for countries worldwide. It can help clarify whether China’s regional emergency logistics response capacity can effectively guarantee the resources needed for foreign direct investment (FDI). Understanding this impact can also offer decision-making insights to market participants in international direct investment by enabling scientific regional selection. Specifically, it can accurately assess the probability of direct investment risks in different regions, thereby reducing the risks associated with international direct investment and enhancing its efficiency.

In the study of how emergency logistics response capacity affects the sustainability of economic growth, it was found that, from the national sample observation, the improvement of national emergency logistics response capability helps enhance economic growth’s sustainability. From a regional perspective, it was observed that the emergency logistics response capabilities in China’s diverse regions can effectively respond to public emergencies and ensure the sustainability of economic growth. Among them, the eastern region’s emergency logistics response capacity has a higher promotion effect on the sustainability of economic growth than the central region. In contrast, the western region has the lowest effect.

In addition, from the perspective of the impact trend, the impact trend of national emergency logistics response capability on sustainable economic growth shows an interesting pattern. The overall performance shows a decrease and then an increase. Moreover, there are variations in different regions. Generally, the promotion of emergency logistics response capacity in eastern and central China has positively contributed to the sustainability of economic growth. Notably, the eastern region has experienced a higher effect compared to the central region.

Finally, based on the regression results of the panel threshold model, it is evident that to enhance the response capacity of emergency logistics, China’s emergency management departments may have invested excessively in emergency logistics resources. In terms of regional analysis, the improvement of emergency logistics response capacity in eastern China has been demonstrated to effectively address the impact of public emergencies on the sustainability of economic growth. As emergency logistics response capacity enhances, public emergencies’ inhibitory effect on economic growth’s sustainability decreases. Particularly, reducing this inhibitory effect is more prominent in the central region than in the eastern region, as the improvement in emergency logistics response capacity has a more substantial impact.

Conclusions and implications

Research conclusions

The research summarized constituents of emergency logistics response capacity following its connotation. These components include emergency logistics carrier carrying capacity, social emergency support capacity, emergency logistics flow capacity, emergency logistics flow efficiency capacity, and regional information capacity. Combining the index system constructed in this paper, EWTOPSIS employed to calculate the emergency logistics response capacity of 30 provinces (municipalities and autonomous regions) in China from 2012 to 2021. This paper examines the influence of China’s regional capacity for emergency logistics response on sustainable economic growth. Specifically, it focused on the effect of emergency logistics response capability on economic growth sustainability, the relationship between public emergencies and emergency logistics response capability, the tendency of regional emergency logistics response capability’s impact on economic growth sustain-ability, and the mechanism through which it affects economic growth sustainability. This study also built a panel regression model, a panel quantile regression model, and a panel threshold model applied to assess how emergency logistics response capability affects the ability to keep economic growth going in the face of public emergencies.

The empirical results provide clear evidence that the emergency logistics response capability across China’s provinces significantly improved from 2012 to 2021. Notably, the most rapid growth occurred between 2017 and 2018, which can be attributed to the establishment of the Ministry of Emergency Management in 2018. Moreover, there is a clear imbalance in emergency logistics response capability across different geographical divisions in China. Compared to other regions, the eastern region demonstrates significantly greater strength, while the provinces in western China have relatively weaker emergency logistics response capabilities.

In addition, from the perspective of the impact of emergency logistics response capability on the sustainability of economic growth, it is evident from the national sample that enhancing China’s emergency logistics response capability contributes to enhancing the sustainability of economic growth. Moreover, this suggests that during public emergencies, China’s emergency management departments are capable of efficiently converting social logistics into emergency logistics response capabilities. It can effectively alleviate or eliminate the negative impact of public emergencies on sustainable economic growth and prevent further escalation. It is worth noting that the impact of response capability on the sustainability of economic growth also varies by region. Among them, eastern China shows a greater positive influence on sustainable economic growth compared to the central region, while the western region exhibits the lowest effect. This reflects the varying levels of management within the emergency management department when adapting social logistics into emergency logistics response capacity, with the eastern region showing the highest level and the western region showing the weakest.

Furthermore, this study reveals a key trend: nationwide, as emergency logistics response capability increases, its positive impact on the sustainability of economic growth shows a trend of first decreasing and then increasing. However, it is important to note that the regional dynamics of sustainable growth and response capability are not the same. The continuously rising positive impact is particularly evident in the eastern and central regions, with eastern China outperforming the central region. This suggests that the eastern region has more abundant social logistics resources, and even after adapting social logistics into emergency logistics, the emergency management departments in eastern China still have relatively sufficient social logistics resources, or the remaining social logistics services continue to operate more efficiently for society. However, in western China, the impact of improved emergency logistics response capability on the sustainability of economic growth shows a reverse trend of first increasing and then decreasing. This indicates that there may be an over-conversion of social logistics into emergency logistics in the western region, thereby continuously encroaching on the remaining capacity of social logistics to support economic growth.

Finally, the regression results of the panel threshold model reveal a potential issue of resource overinvestment by China’s emergency management departments when enhancing emergency logistics response capability. Under the influence of such excessive investment, public emergencies can positively impact the sustainability of economic growth when emergency logistics response capacity falls below a certain threshold. However, there are also regional differences. In central China, as emergency logistics response capability improves, the reduction in the suppressive effect of public emergencies on the sustainability of economic growth is stronger than in the eastern region. Nonetheless, in both regions, the suppressive effect of public emergencies on the sustainability of economic growth decreases with the strengthening of emergency logistics response capability.

Research implications

This paper analyzed the impact of China’s regional emergency logistics response capacity on the sustainability of economic growth. This study has certain deficiencies and limitations due to time constraints and challenges in obtaining some data, which require further investigation. Firstly, besides exploring the impact of the relationship between public emergencies and emergency logistics response capabilities on the sustainability of economic growth, it is crucial to consider the varying response levels required by different types of public emergencies, each with unique demands for response capabilities. However, this paper has yet to provide further analysis on this issue. Public data on the classification and response level indicators for different types of public emergencies is not sufficiently detailed. Hence, it is necessary to break down and expand this aspect further in future research. Secondly, when studying the impact of emergency logistics response capability on the sustainability of economic growth, it is crucial to analyze the relationship between the components of emergency logistics response capability and public emergencies. This relationship and its impact on the sustainability of economic growth have yet to be explored in this paper due to its length and time constraints. It is recommended to examine this aspect further in future research.