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Data Science in Finance and Economics

2022, Volume 2, Issue 4: 371-390. doi: 10.3934/DSFE.2022019
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Research article

How does the digital economy affect industrial eco-efficiency? Empirical evidence from China

  • School of Statistics, Lanzhou University of Finance and Economics, Lanzhou, China

  • Industry is a sector with large energy consumption and pollutant emissions. Improving industrial eco-efficiency is crucial to energy conservation and pollution reduction. The digital economy has developed rapidly in recent years. However, there is a lack of research on the specific relationship between the digital economy and industrial eco-efficiency. This study measured the industrial eco-efficiency of 30 provinces in China from 2010 to 2020, through a super-efficiency slack-based measure (SBM) considering desirable outputs. By constructing a two-way fixed effect model and a panel quantile model, this study explored the effects of the digital economy on industrial eco-efficiency on a national scale. Furthermore, this study conducted grouping regression and investigated the heterogeneous impacts of the digital economy on industrial eco-efficiency. Finally, this study built a spatial Durbin model to explore the spatial effects of digital economy on industrial eco-efficiency. According to the empirical results, this study yielded the following conclusions. First, the digital economy has a significantly positive effect on industrial eco-efficiency at the national scale, with diminishing marginal returns. Second, the effects of the digital economy on industrial eco-efficiency are significantly heterogeneous on a regional scale. For eastern regions, the effects of the digital economy on industrial eco-efficiency are significantly positive, while they are negative for western regions. Third, the spillover effect of the digital economy on industrial eco-efficiency is not significant in China, indicating that there is digital isolation.

    Citation: Lu Liu, Ming Liu. How does the digital economy affect industrial eco-efficiency? Empirical evidence from China[J]. Data Science in Finance and Economics, 2022, 2(4): 371-390. doi: 10.3934/DSFE.2022019

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  • Industry is a sector with large energy consumption and pollutant emissions. Improving industrial eco-efficiency is crucial to energy conservation and pollution reduction. The digital economy has developed rapidly in recent years. However, there is a lack of research on the specific relationship between the digital economy and industrial eco-efficiency. This study measured the industrial eco-efficiency of 30 provinces in China from 2010 to 2020, through a super-efficiency slack-based measure (SBM) considering desirable outputs. By constructing a two-way fixed effect model and a panel quantile model, this study explored the effects of the digital economy on industrial eco-efficiency on a national scale. Furthermore, this study conducted grouping regression and investigated the heterogeneous impacts of the digital economy on industrial eco-efficiency. Finally, this study built a spatial Durbin model to explore the spatial effects of digital economy on industrial eco-efficiency. According to the empirical results, this study yielded the following conclusions. First, the digital economy has a significantly positive effect on industrial eco-efficiency at the national scale, with diminishing marginal returns. Second, the effects of the digital economy on industrial eco-efficiency are significantly heterogeneous on a regional scale. For eastern regions, the effects of the digital economy on industrial eco-efficiency are significantly positive, while they are negative for western regions. Third, the spillover effect of the digital economy on industrial eco-efficiency is not significant in China, indicating that there is digital isolation.


    1.   Introduction

    The mathematical models in epidemiology have been used to understand the temporal dynamics of infectious diseases. The first model used to study the spread of infectious diseases was given by Kermack and Mckendrick [1] in 1927. Practically, this model is based on a system of ordinary differential equations and has been widely investigated with several modifications, in [2]–[7] and references therein. The distinct variables to formulate the individuals compartments are susceptible (S), exposed (E), infected (I) and recovered (or removed, R). The classical SEIR model has been widely studied, for instance, see [8]–[13]. It is shown that the asymptotic behavior depends on the basic reproduction number ℜ0 (the expected number of secondary cases produced by an infective person in a completely susceptible population). It is described as a threshold value that indicates whether or not the initial outbreak occurs. That is, if ℜ0 < 1, then the infective population tends to decrease and there is no outbreak, whereas if ℜ0 > 1, then the infective population tends to increase and an outbreak occurs.

    In December 2019, the first case of a novel coronavirus disease was recognized at Wuhan in China [14]. The wave of this disease has spread all over the world, and the World Health Organization (WHO) named it the coronavirus disease outbreak in 2019 (COVID-19) on 11 February, 2020 [14]. In China, during the period from December 2019 to 31 January, 2020, about 10 thausand (9,720) cases of COVID-19 were confirmed [14]. We have to note that asymptomatic individuals of COVID-19 can transmit the infection [15]. Therefore, there would be more cases that could not be reported by medical authorities. In the absence of effective vaccines and therapeutics against COVID-19, countries have to resort to non-pharmaceutical interventions to avoid the infection or to slow down the spread of the epidemic.

    In Algeria, the first case was reported on 25 February 2020 [16]. Since then, the number of confirmed cases of COVID-19 has increased day after day. From the end of March, 2020, the Algerian government mandated several approaches to eradicate the spread of COVID-19 such as trying to control the source of contagion and reducing the number of contacts between individuals by confinement and isolation [17]. The purpose of this study is to know how the epidemic will evolve in Algeria with and without such interventions. We use the early data reported in [18] until 31 March, 2020. Recently, many works used a mathematical models for COVID-19, for instance, see the following contributions [19]–[23]. In [20], an SEIR epidemic model with partially identified infected individuals was used for the prediction of the epidemic peak of COVID-19 in Japan. The results in [20] were restricted only to the cases in Japan, and the applicability of the model to the cases in any other countries were not discussed. In this paper, we apply a similar SEIR epidemic model as in [20] to the cases in Algeria. This work would contribute not only in understanding the possible spread pattern of COVID-19 in Algeria in order to act appropriately to reduce the epidemic damage, but also in showing the applicability of the model-based approach as in [20] to the cases in other countries, which might help us to assess the epidemic risk of COVID-19 worldwide in future.

    2.   Materials and method

    2.1.   Data

    We use the data of confirmed COVID-19 cases in Algeria, which is available in the epidemiological map in [18]. The data consists of the daily reported number of new cases and accumulated cases for COVID-19 in Algeria from 25 February to 18 April, 2020 (see Figure 1 and Table 1). The number of reported cases has increased rapidly in the exponential sense until the beginning of April.

    Table 1.  Number of newly reported cases and cumulative number of COVID-19 in Algeria from 25 February to 18 April, 2020 with the nationwide isolation. From 25 February to 18 April 2020.
    Date (day/month) Number of newly reported cases Cumulative number
    25 February 1 1
    26 February 0 1
    27 February 0 1
    28 February 0 1
    29 February 2 3
    1 March 0 3
    2 March 0 3
    3 March 2 5
    4 March 12 17
    5 March 0 17
    6 March 0 17
    7 March 2 19
    8 March 1 20
    9 March 0 20
    10 March 0 20
    11 March 0 20
    12 March 5 25
    13 March 0 25
    14 March 10 35
    15 March 17 52
    16 March 6 58
    17 March 2 60
    18 March 12 72
    19 March 18 90
    20 March 12 102
    21 March 37 139
    22 March 60 201
    23 March 29 230
    24 March 34 264
    25 March 38 302
    26 March 65 367
    27 March 42 409
    28 March 45 454
    29 March 57 511
    30 March 73 584
    31 March 132 716
    1 April 131 847
    2 April 139 986
    3 April 185 1171
    4 April 80 1251
    5 April 69 1320
    6 April 103 1423
    7 April 45 1468
    8 April 104 1572
    9 April 94 1666
    10 April 95 1761
    11 April 64 1825
    12 April 89 1914
    13 April 69 1983
    14 April 87 2070
    15 April 90 2160
    16 April 108 2268
    17 April 150 2418
    18 April 116 2534
     | Show Table
    DownLoad: CSV
    Figure 1.  Daily reported number of new cases (left) and accumulated cases (right) of COVID-19 in Algeria from 25 February to 18 April, 2020 [18].
    DownLoad: Full-Size Img PowerPoint

    2.2.   Model

    In this paper, we use the following well-known SEIR epidemic model, for t > 0,

    {S′(t)=−βS(t)I(t),E′(t)=βS(t)I(t)−λE(t),I′(t)=−γI(t)+λE(t),R′(t)=γI(t),
    with initial conditions
    S(0)=S0, E(0)=E0, I(0)=I0 and R(0)=R0.
    (1)–(2) is a system of ordinary differential equations based on the phenomenological law of mass action. For simplicity, we suppose that E0 = R0 = 0 (initially, there is no exposed and recovered individual). Moreover, we assume that S(0) + E(0) + I(0) + R(0) = 1 from which we have S(t) + E(t) + I(t) + R(t) = 1 for all t > 0, and hence, each population implies the proportion to the total population. All the parameters of the model are nonnegative constants, and they are described in Table 2. Figure 2 provides a schematic representation of model (1).

    Figure 2.  Interactions between the compartments of the epidemiological model (1). The continuous lines represent transition between compartments, and entrance and exit of individuals. The dashed line represents the transmission of the infection through the interaction between susceptible and infected individuals. The recovered class is omitted because it is decoupled from the other compartments.
    DownLoad: Full-Size Img PowerPoint

    Note that γ implies the removal rate and the removed population R includes the individuals who died due to the infection.

    We follow the same idea in [20] to give the prediction for Algeria. Parameter estimation and epidemic peak are treated and obtained. Moreover, we estimate the basic reproduction number for the epidemic COVID-19 in Algeria. Since the virus presents asymptomatic cases and the fact that there is a sufficiently lack of diagnostic test, we consider an identification function

    t∈ℜ+↦X(t)=ϵ×I(t)×N.

    This quantity describes the number of infective individuals who are identified at time t, with N is the total population in Algeria (N = 43411571) and ε is the identification rate. As in [20], we suppose that ε ∈ [0.01, 0.1].

    3.   Results

    3.1.   Epidemic prediction

    In this section, we develop simulations to provide epidemic predictions for the COVID-19 epidemic in Algeria. We focus on predicting the cases and parameter estimation. We are able to find the basic reproduction number and to estimate the infection rate. Recall that the number ℜ0 is defined as the average number of secondary infections that occur when one infective individual is introduced into a completely susceptible population. In epidemiology, the method to compute the basic reproduction number using the next-generation matrix is given by Diekmann et al. [24] and Van den Driessche and Watmough [25]. For our model, the value of the basic reproduction number of the disease is defined by

    ℜ0=βS0γ=βγ(1−E0−I0−R0)=βγ(1−X(0)ϵN).

    In fact, the largest eigenvalue or spectral radius of FV−1 is the basic reproduction number of the model, where

    F=[0βS000] and V=[λ0−λγ].
    In our case, we assume that X(0) = 1 and N = 43411571, see Tables 1 and 2.

    By some choices on the parameter ε, illustrations for prediction are given in Figures 3, 4 and 5. To estimate the parameters, we use the method of least squares and the best fit curve that minimizes the sum of squared residuals. We remark that ε does not affect the basic reproduction number and the infection rate as the total population N is large. We obtain an estimation of them as shown in Table 2 (ℜ0 = 4.1 and β = 0.41). However, we observe that ε is an important parameter for prediction. In fact, the three illustrations in Figures 3, 4 and 5 show its influence on the peak.

    As stated before, we use a simple but useful measure to provide the average number of infections caused by one infected individual R0 = 4.1. The R0 value in China was estimated to be around 2.5 in the early stage of epidemic. In April 2020, the contagiousness rate was reassessed upwards, between 3.8 and 8.9 (see, [26]). Comparing with other results (see, [27]), R0 may be unstable.

    Table 2.  Parameter values for numerical simulation.
    Description Value Reference
    β: Contact rate 0.41 Estimated
    γ: Removal rate 0.1 [28], [29]
    λ: Onset rate 0.2 [14], Situation report 30, [28], [30]
    1/γ: The average infectious period 10 [28], [29]
    1/λ: The average incubation period 5 [14], Situation report 30, [28], [30]
    ε: Identification rate 0.01–0.1 [20]
    N: Total population in Algeria 43411571 [31]
    ℜ0: Basic reproduction number 4.1 Estimated
     | Show Table
    DownLoad: CSV
    Figure 3.  Graph of X(t), with ε = 0.01, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 100 associated to a date between the beginning and the middle of June.
    DownLoad: Full-Size Img PowerPoint
    Figure 4.  Graph of X(t), with ε = 0.05, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 110 associated to a date close to the middle of June.
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Graph of X(t), with ε = 0.1, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 115 associated to a date between the middle and the end of June.
    DownLoad: Full-Size Img PowerPoint

    3.2.   Intervention effect

    We now discuss the effect of intervention. We first assume that the intervention is carried out for two months from April 1 (t = 37) to May 31 (t = 96) with reducing the contact rate β to kβ, where 0 ≤ k ≤ 1. We use the parameter values as in Table 2 with ε = 0.1. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 is displayed in Figure 6.

    Figure 6.  Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the two months intervention from April 1 (t = 37) to May 31 (t = 96).
    DownLoad: Full-Size Img PowerPoint

    From Figure 6, we see that the two months intervention in this case has the positive effect on the time delay of the epidemic peak. On the other hand, the epidemic size is almost the same for each k in this case.

    We secondly assume that the intervention is carried out for three months from April 1 (t = 37) to June 30 (t = 126).

    From Figure 7, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is reduced for k = 0.75. In contrast, the epidemic size for k = 0.5 and k = 0.25 is almost the same as that for k = 1.

    Figure 7.  Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the three months intervention from April 1 (t = 37) to June 30 (t = 126).
    DownLoad: Full-Size Img PowerPoint

    We thirdly assume that the intervention is carried out for four months from April 1 (t = 37) to July 31 (t = 157).

    Figure 8.  Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the four months intervention from April 1 (t = 37) to July 31 (t = 157).
    DownLoad: Full-Size Img PowerPoint

    From Figure 8, we see that the epidemic peak is also delayed in this case. On the other hand, similar to the example for three months intervention, the epidemic size is effectively reduced only for k = 0.75.

    We fourthly assume that the intervention is carried out for five months from April 1 (t = 37) to August 31 (t = 188). From Figure 9, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is effectively reduced for k = 0.75 and k = 0.5. In contrast, the epidemic size for k = 0.25 is almost the same as that for k = 1.

    Figure 9.  Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the five months intervention from April 1 (t = 37) to August 31 (t = 188).
    DownLoad: Full-Size Img PowerPoint

    We finally assume that the intervention is carried out for six months from April 1 (t = 37) to September 30 (t = 218). From Figure 10, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is effectively reduced for k = 0.75 and k = 0.5. In contrast, the epidemic size for k = 0.25 is almost the same as that for k = 1.

    Figure 10.  Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the six months intervention from April 1 (t = 37) to September 30 (t = 218).
    DownLoad: Full-Size Img PowerPoint

    From these examples, we obtain the following epidemiological insights.

    • The epidemic peak is delayed monotonically as k decreases (that is, the contact rate is reduced by the intervention).
    • The epidemic size is not necessarily reduced even if a long and strong intervention is carried out. To effectively reduce the epidemic size by the intervention, it suffices to continue the intervention until the epidemic peak attains during the intervention period.

    In Figure 11, we look at the case of measures that are more effective. Our model shows that the end of the disease can be reached in the three months intervention, from April 1, for k = 0.05 and in the four months intervention for k = 0.1, from April 1. More severe measures can induce an end of the disease in two months (see Figure 12 (a) with k = 0.01).

    Figure 11.  Case of a very strong intervention, with ε = 0.1. The red small circles are the reported case data. Left: time variation of the identification function X(t) for k = 0.05 in the three months intervention, from April 1 (t = 37). Right: time variation of the identification function X(t) for k = 0.1 in the four months intervention, from April 1 (t = 37).
    DownLoad: Full-Size Img PowerPoint

    In Figure 12, we consider the cases where interventions are not taken early. It is shown that a delay in intervention implies a larger peak and additional duration for the epidemic to disappear. For instance, an intervention from April 20 implies a supplementary delay by 23 days for the epidemic to disappear with high considerable peak.

    Figure 12.  Simulation of different start times of carrying out the measures, with ε = 0.1 and k = 0.01 (Case of a very strong intervention). The red small circles are the reported case data. (a) from April 1 (t = 37), (b) from April 10 (t = 46), (c) from April 20 (t = 56) and (d) from April 30 (t = 66).
    DownLoad: Full-Size Img PowerPoint

    For some economical and social reasons and according to the situation of the epidemic, the strictness of intervention measures will decrease in a gradual way. The Figure 13 shows the case where the severity of intervention measures is reduced. This simulation suggests that decrease the parameter k gradually, on each half month, implies automatically a delay, at least of one month, for the end of the epidemic.

    Figure 13.  Simulation of the case where the strictness of intervention measures is reduced. The red small circles are the reported case data. From April 1 (t = 37), we take k = 0.01. From April 15 (t = 52), we take k = 0.05. From April 30 (t = 67), we take k = 0.1.
    DownLoad: Full-Size Img PowerPoint

    4.   Conclusion

    We have applied a mathematical model to predict the evolution of a COVID-19 epidemic in Algeria. It is employed to estimate the basic reproduction number ℜ0, to obtain the epidemic peak and to discuss the effect of interventions. In this model, we take the fact that the virus presents asymptomatic cases and that there exists a sufficiency lack of diagnostic test.

    The prognostic capacity of our model requires a valid values for the parameters β, λ, γ, the mean incubation period 1/λ and the mean infectious period 1/γ. The precision of these parameters is very important for predicting the value of the basic reproduction number ℜ0 and the peak of the epidemic. Their estimations depend on the public health data in Algeria. To fight the new coronavirus COVID-19, it is necessary to control information based on valid diagnosis system.

    From 25 February to 31 March, we founded that ℜ0 = 4.1 > 1, which means that we need strong interventions to reduce the epidemic damage that could be brought by the serious disease. Moreover, the model suggests that the pandemic COVID-19 in Algeria would not finish at a fast speed.

    In the Figures 3, 4 and 5, where ε = 0.01, 0.05, 0.1, respectively, the data from the number of newly reported cases is well fitted the epidemic. The peak will occur at the month of June and approximately close to the middle, the maximum number of new cases (relatively also the cumulative number) could achieve an important value in Algeria. This number will probably persist at a high level for several days if we do not apply intervention measures (isolation, quarantine and public closings). The model's predictions highlight an importance for intervening in the fight against COVID-19 epidemics by early government action. To this end, we have discussed different intervention scenarios in relation to the duration and severity of these interventions. We see that the intervention has a positive effect on the time delay of the epidemic peak. On the other hand, the epidemic size is almost the same for short intervention (effective or not) and decrease depending on the severity of the measures. In contrast with the last previous case, we observe that a large epidemic can occur even if the intervention is long and sufficiently effective.

    At the moment, the consequence of COVID-19 in China is encouraging for many countries where COVID-19 is starting to spread. Despite the difficulties, Algeria must also implement the strict measures as in Figure 11, which could be similar to the one that China has finally adopted.



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