
Recently, European regulation on sustainability preferences has made green finance a mainstream topic for retail investors. On the contrary, green innovation is largely discussed as bearing risks, and renewable energy projects are sometimes referred to as related to high risk. Our article aimed to shed light on retail investors' risk exposure in green finance. In the literature review, we rarely found the retail investor's risk perspective reflected, and green finance risk in terms of major capital loss was not explicitly stated as a research topic. We aimed to close this gap in the literature and apply a multiple case study approach with cases from the renewable energy sector to analyze the components that nurture green finance risk. For case description, we leveraged publicly available online information such as press articles, financial reporting, mandatory disclosure from the represented company, and pre-contractual information of the financial instruments marketed. Our findings suggest that green finance risk (GFR) is nurtured by risk components from the categories of financial instrument risk (FIR), investee company risk (ICR), and operational risk (OR) of renewable energy projects. The cross-case analysis identified red flags that might alert future investors. Additionally, we suggested measures to mitigate green finance risk and propose regulatory improvements. Our research marks a starting point for future quantitative and qualitative research.
Citation: Laura Grumann, Mara Madaleno, Elisabete Vieira. The green finance dilemma: No impact without risk – a multiple case study on renewable energy investments[J]. Green Finance, 2024, 6(3): 457-483. doi: 10.3934/GF.2024018
[1] | Zhichao Jiang, Xiaohua Bi, Tongqian Zhang, B.G. Sampath Aruna Pradeep . Global Hopf bifurcation of a delayed phytoplankton-zooplankton system considering toxin producing effect and delay dependent coefficient. Mathematical Biosciences and Engineering, 2019, 16(5): 3807-3829. doi: 10.3934/mbe.2019188 |
[2] | Ruiqing Shi, Jianing Ren, Cuihong Wang . Stability analysis and Hopf bifurcation of a fractional order mathematical model with time delay for nutrient-phytoplankton-zooplankton. Mathematical Biosciences and Engineering, 2020, 17(4): 3836-3868. doi: 10.3934/mbe.2020214 |
[3] | Juan Li, Yongzhong Song, Hui Wan, Huaiping Zhu . Dynamical analysis of a toxin-producing phytoplankton-zooplankton model with refuge. Mathematical Biosciences and Engineering, 2017, 14(2): 529-557. doi: 10.3934/mbe.2017032 |
[4] | Fang Liu, Yanfei Du . Spatiotemporal dynamics of a diffusive predator-prey model with delay and Allee effect in predator. Mathematical Biosciences and Engineering, 2023, 20(11): 19372-19400. doi: 10.3934/mbe.2023857 |
[5] | Elvira Barbera, Giancarlo Consolo, Giovanna Valenti . A two or three compartments hyperbolic reaction-diffusion model for the aquatic food chain. Mathematical Biosciences and Engineering, 2015, 12(3): 451-472. doi: 10.3934/mbe.2015.12.451 |
[6] | Zhenyao Sun, Da Song, Meng Fan . Dynamics of a stoichiometric phytoplankton-zooplankton model with season-driven light intensity. Mathematical Biosciences and Engineering, 2024, 21(8): 6870-6897. doi: 10.3934/mbe.2024301 |
[7] | Kawkab Al Amri, Qamar J. A Khan, David Greenhalgh . Combined impact of fear and Allee effect in predator-prey interaction models on their growth. Mathematical Biosciences and Engineering, 2024, 21(10): 7211-7252. doi: 10.3934/mbe.2024319 |
[8] | Yuanpei Xia, Weisong Zhou, Zhichun Yang . Global analysis and optimal harvesting for a hybrid stochastic phytoplankton-zooplankton-fish model with distributed delays. Mathematical Biosciences and Engineering, 2020, 17(5): 6149-6180. doi: 10.3934/mbe.2020326 |
[9] | Kalyan Manna, Malay Banerjee . Stability of Hopf-bifurcating limit cycles in a diffusion-driven prey-predator system with Allee effect and time delay. Mathematical Biosciences and Engineering, 2019, 16(4): 2411-2446. doi: 10.3934/mbe.2019121 |
[10] | Yuhong Huo, Gourav Mandal, Lakshmi Narayan Guin, Santabrata Chakravarty, Renji Han . Allee effect-driven complexity in a spatiotemporal predator-prey system with fear factor. Mathematical Biosciences and Engineering, 2023, 20(10): 18820-18860. doi: 10.3934/mbe.2023834 |
Recently, European regulation on sustainability preferences has made green finance a mainstream topic for retail investors. On the contrary, green innovation is largely discussed as bearing risks, and renewable energy projects are sometimes referred to as related to high risk. Our article aimed to shed light on retail investors' risk exposure in green finance. In the literature review, we rarely found the retail investor's risk perspective reflected, and green finance risk in terms of major capital loss was not explicitly stated as a research topic. We aimed to close this gap in the literature and apply a multiple case study approach with cases from the renewable energy sector to analyze the components that nurture green finance risk. For case description, we leveraged publicly available online information such as press articles, financial reporting, mandatory disclosure from the represented company, and pre-contractual information of the financial instruments marketed. Our findings suggest that green finance risk (GFR) is nurtured by risk components from the categories of financial instrument risk (FIR), investee company risk (ICR), and operational risk (OR) of renewable energy projects. The cross-case analysis identified red flags that might alert future investors. Additionally, we suggested measures to mitigate green finance risk and propose regulatory improvements. Our research marks a starting point for future quantitative and qualitative research.
Blooms are considered to be a rapid and significant increase in marine phytoplankton populations. It is generally believed that zooplankton is the primary predator of blooms. The zooplankton dissolves phytoplankton blooms in the bud by feeding a large amount of phytoplankton, thereby achieving the purpose of preventing and controlling algal blooms. It is clearly stated in an international research plan that the dynamic changes of zooplankton in marine ecosystems control the change in the total amount of primary productivity. This indicates that zooplankton plays a very important role in the production of phytoplankton and the control of existing quantities. In addition, this is also one of the significant reasons that the interaction between phytoplankton and zooplankton has been widely concerned by many scholars [1,2,3,4,5,6] in recent years.
Due to the importance of the interaction between phytoplankton and zooplankton in marine biological systems, many scholars have proposed a large number of models to describe this interaction. Moreover, they have continued to study this topic for the past few decades and have achieved very significant results [7,8,9,10,11,12,13]. Chakraborty et al. [7] proposed a mathematical model incorporating nutrient concentration, toxin producing phytoplankton (TPP), non-toxic phytoplankton (NTP), and toxin concentration and explained that TPP species controls the outbreaks of other NTP species when nutrient-deficient conditions are beneficial for the TPP species to release toxin. Saha and Bandyopadhyay [8] analyzed a toxin producing phytoplankton-zooplankton model in which the release of toxins from phytoplankton species follows a discrete time variation and discussed the basic dynamics of the system. Banerjee and Venturino [9] proposed a phytoplankton-toxic phytoplankton-zooplankton model and found that toxic phytoplankton population does not cause zooplankton population to become extinct. Javidi and Ahmad [10] investigated the dynamics of a time fractional order toxic-phytoplankton-phytoplankton-zooplankton system(TPPZS) and did some numerical simulations to validate the theoretical results. Han and Dai [11] studied the spatiotemporal pattern caused by cross-diffusion of a toxic-phytoplankton-zooplankton model with nonmonotonic functional response and discussed the effect of toxin-producing rate of toxic-phytoplankton (TPP) species and natural mortality rate of zooplankton species on pattern selection. In addition, Han and Dai proposed a spatiotemporal pattern formation and selection driven by nonlinear cross-diffusion of a toxic-phytoplankton-zooplankton model with Allee effect in [12]. Zheng and Sugie [13] studied a three-dimensional system including phytoplankton, zooplankton and fish and gave a sufficient condition to ensure that the equilibrium of this three-dimensional system is globally asymptotically stable. Furthermore, they also proved that the equilibrium is asymptotically stable under relatively weak conditions.
Because the seabed has a variety of sediments that can be used as a refuge for the prey, some phytoplankton populations reduce the risk of being caught by zooplankton through refuge. Therefore, some scholars [14,15,16,17,18] have considered the prey refuge in the predator-prey model in recent years. Kar [14] proposed a prey-predator model incorporating a prey refuge and studied the influence of prey refuge on the prey-predator model. Chen and Chen [15] investigated a predator-prey model with Holling type Ⅱ functional response incorporating a constant prey refuge and gave the basic dynamical behaviors of the model. Tripathi et al. [16] studied a prey-predator model with reserved area and found that the predator species would exist if the value of prey reserve does not exceed a threshold value, beyond which the predator species would become extinct. Ghosh et al. [17] analyzed the effect of additional food for predator on the dynamical behaviors of a prey-predator model with prey refuge, they suggested that the possibility of extinction of predator species in high prey refuge ecosystems can be eliminated by providing additional food to predator species. Samanta et al. [18] proposed a fractional-order prey-predator model with prey refuge and derived some sufficient conditions to guarantee the global asymptotic stability of predator-extinction equilibrium and co-existing equilibrium.
Li et al. [19] considered the factors that phytoplankton protects themselves from being eaten by zooplankton by releasing toxin and taking refuge, and proposed a toxic phytoplankton-zooplankton model with refuge
{.P(t)=rP(1−PK)−β1(P−m)a1+(P−m)Z,.Z(t)=β2(P−m)a1+(P−m)Z−dZ−θPa2+PZ, | (1.1) |
where P(t)andZ(t) denote population size of phytoplankton and zooplankton at time t, respectively; r is the intrinsic growth rate; K is the environmental carrying capacity in the absence of zooplankton; β1, β2 (0<β2<β1) and d describe the predation rate, the ratio of biomass conservation and the natural death rate of zooplankton species, respectively; a1 and a2 are the half saturation constants; θ represents the rates of toxin production per phytoplankton species; m is the constant refuge capacity of phytoplankton population, then P−m denotes the number of unprotected phytoplankton is captured by zooplankton species. Their results ultimately showed that phytoplankton refuge and toxin have important effects on the occurrence and termination of algal blooms in freshwater lakes.
Actually, the choice of prey species growth function and predator species function response is considered to be the most important element in the prey-predator model. Generally speaking, many modelers choose Logistic growth form as prey species growth function without considering the predator species. However, we all know that the resources in an ecosystem are limited, such as space, food, basic nutrition and so on. Thus the average growth rate becomes a decreasing function of population size as the population size increases gradually. When the number of population reaches the environmental capacity K, the average growth rate decreases to zero; in addition, any population number above the value K will have a negative growth rate. However, there are a lot of evidences that the low population density is the opposite [20,21,22,23,24,25]. This phenomenon is the so-called Allee effect, which is the positive density dependence of population growth at low density [22,26]. The main causes of the Allee effect are the lack of a spouse, the reduced vigilance against predators, the adjustment of the environment, the reduction of defenses against predators, and many other reasons [20,22]. In general, the Allee effect is expressed by an equation of the following form [27]
dX(t)dt=rX(t)(1−X(t)K)(X(t)K0−1), |
where X(t) represents population size at time t; r and K are the intrinsic growth rate and the environmental carrying capacity, respectively; K0 is the critical level of phytoplankton population. When the population density is below the critical level, the population growth rate will decline and population will tend to become extinct.
In recent years, more and more people consider time delay into population biological model [28,29,30,31,32,33,34,35] to study its influence on dynamical behavior of system. What's more, it is well known that most countries of the world achieve the economic benefits of natural resource management by restoring and maintaining the ecosystem's health, the productivity and biodiversity and the overall quality of life in a manner that integrates social and economic objectives. Of course, it also satisfies the need for humans to benefit from natural resources. The prey-predator systems with harvesting have been widely studied by a large number of scholars [6,34,35,36,37,38,39,40]. Meng et al. [40] studied a predator-prey system with harvesting prey and disease in prey species and found that the optimal harvesting effort is closely related to the incubation period of the infectious disease, and the maximum value of the optimal harvesting decreases with the increase of the time delay. Furthermore, Meng et al. [34] investigated a nutrient-plankton model with Holling type Ⅳ, delay, and harvesting, their results indicated that the nutrient increases first and then stabilizes as the harvesting time increases, the number of phytoplankton and zooplankton decreases and even stabilizes as the harvesting time increases. Zhang and Zhao [36] considered a diffusive predator-prey system with delays and interval biological parameters and believed that the overfishing can lead to species extinction.
Motivated by the work mentioned-above, we propose a delayed phytoplankton-zooplank-ton model with Allee effect and linear harvesting. The results of this paper can be seen as a complement to system (1.1). As far as we know, although a large number of scholars have studied the interaction between phytoplankton and zooplankton, system (1.1) including the Allee effect, time delay and linear harvesting has not been studied yet.
The content of this paper is organized as follows. A delayed phytoplankton-zooplankton model with Allee effect and linear harvesting is described in section 2. In this section, we give the boundedness of the model. Section 3 demonstrates the existence and stability of the equilibria of system (2.1). A detailed discussion of the Hopf bifurcation in section 4. We not only give the existence and property of Hopf bifurcation when the system has no time delay, but also give the existence and property of Hopf bifurcation when the system has time delay. In section 5, the optimal policy is derived by using Pontryagin's Maximum Principle. In section 6, some numerical simulations are given for illustrating the theoretical results. The problem ends with a brief concluding remark.
In this paper, based on the work of the reference [19], we propose a delayed phytoplankton-zooplankton system with Allee effect and linear harvesting described by
{.P(t)=rP(1−PK)(PK0−1)−β1(P−m)Za1+(P−m)−q1EP,.Z(t)=β2(P(t−τ)−m)Za1+(P(t−τ)−m)−dZ−θPZa2+P−q2EZ, | (2.1) |
where K0 (0<K0<<K) is the critical level of the growth of phytoplankton; q1 and q2 are catchability coefficients of the two species; E represents the harvesting effort. The delay τ in system (2.1) can be regarded as the maturation period of zooplankton species. For biological significance, system (2.1) must satisfy the following initial conditions
P(θ)=ϑ1(θ)≥m,Z(θ)=ϑ2(θ)≥0,ϑ1(0)>m,ϑ2(0)>0,θ∈[−τ,0], |
where (ϑ1(θ),ϑ2(θ))∈C([−τ,0],R2+) and C([−τ,0],R2+) represents the Banach space formed by all continuous functions from [−τ,0] to R2+, here R2+={(x1,x2):x1>m,x2>0}.
When τ=0, system (2.1) becomes
{.P(t)=rP(1−PK)(PK0−1)−β1(P−m)Za1+(P−m)−q1EP,.Z(t)=β2(P−m)Za1+(P−m)−dZ−θPZa2+P−q2EZ, | (2.2) |
and system (2.2) must satisfy the following initial conditions
P(0)>m,Z(0)>0. |
As we all know, the boundedness of the model can ensure that the model has good dynamical behaviors. So we have the following result when τ=0.
Lemma 2.1. All solutions of system (2.2) with initial conditions P(0)>m,Z(0)>0 that start in Θ={(P,Z)∣(m,+∞)×(0,+∞)}⊂R2+ are uniformly bounded for all t>0.
Proof. Let (P(t),Z(t)) be any solution of system (2.2) with positive initial conditions P(0)>m, Z(0)>0. Notice that there is Ω1={(P,Z)∣K≥P(t)>m,Z(t)>0 for all t>0} for system (2.2). So we have P(t)≤K for all t>0.
We define W(P,Z)=cP(t)+Z(t), here c=β2β1, thus
.W(t)=c.P(t)+.Z(t)=crP(K+K0KK0P−1−P2KK0)−cq1EP−dZ−θPa2+PZ−q2EZ≤−[dZ+cr(K+K0)KK0P]+2cr(K+K0)KK0P≤−αW+2cr(K+K0)K0, |
where α=min{r,r(K+K0)KK0}.
That is
.W(t)+αW≤2cr(K+K0)K0. |
So,
0<W(P(t),Z(t))≤2cr(K+K0)αK0+e−αtW(P(0),Z(0)). |
When t→∞, we have
0<W≤2cr(K+K0)αK0. |
Therefore, all solutions of system (2.2) enter into the invariant set Ω={(P,Z)∈Θ|0<W≤2cr(K+K0)αK0}. The Lemma 2.1 is proved.
Next, we use the method in [41] to give the positiveness of solution of system (2.1) in the case of τ>0.
Lemma 2.2. All solutions of system (2.1) with initial conditions that start in Θ={(P,Z)∣(m,+∞)×(0,+∞)}⊂R2+ are positive invariant.
Proof. We consider (P,Z) a noncontinuable solution of system (2.1), defined on [−τ,Γ), where Γ∈(0,∞]. We have to prove that for all t∈[0,Γ), P(t)>m, and Z(t)>0. Suppose that is not true. Then, there exists 0<T<Γ such that for all t∈[0,T), P(t)>m, and Z(t)>0 and either P(T)=m or Z(T)=0. For all t∈[0,T), under initial conditions we have
P(t)=P(0)exp{∫t0[r(1−P(s)K)(P(s)K0−1)−β1(P(s)−m)Z(s)P(s)(a1+P(s)−m)−q1E]ds},Z(t)=Z(0)exp{∫t0[β2(P(s)−m)a1+(P(s)−m)−d−θP(s)a2+P(s)−q2E]ds}. |
As (P,Z) is continuous on [−τ,T], there exists a M≥0 such that for all t∈[−τ,T],
P(t)=P(0)exp{∫t0[r(1−P(s)K)(P(s)K0−1)−β1(P(s)−m)Z(s)P(s)(a1+P(s)−m)−q1E]ds}≥P(0)exp(−TM),Z(t)=Z(0)exp{∫t0[β2(P(s)−m)a1+(P(s)−m)−d−θP(s)a2+P(s)−q2E]ds}≥Z(0)exp(−TM). |
Taking the limit, as t→T, we get
P(T)≥P(0)exp(−TM)>m,Z(T)≥Z(0)exp(−TM)>0, |
which contradicts the fact that either P(T)=m, or Z(T)=0. Thus, for all t∈[0,Γ), P(t)>m and Z(t)>0. The Lemma 2.2 is proved.
In order to get the conditions for the existence of the equilibria of system (2.2), we analyze the following Eq (3.1) which is given by
{ rP(1−PK)(PK0−1)−β1(P−m)Za1+(P−m)−q1EP=0,β2(P−m)Za1+(P−m)−dZ−θPZa2+P−q2EZ=0. | (3.1) |
Obviously, the equilibria of system (2.2) are the intersections of the two equations of (3.1). In the absence of zooplankton, that is Z=0, the first equation of (3.1) becomes
rK0KP2−r(K0+K)K0KP+r+q1E=0. |
Let Δ1=r2(K0+K)2−4K0Kr(r+q1E)K20K2, then there is the following conclusion.
Theorem 3.1. The boundary equilibria of system (2.2) are as follows.
(i) If Δ1=0, i.e., E=r(K0−K)24K0Kq1, then system (2.2) has a unique boundary equilibrium given by ˜E1(˜P1,0), here ˜P1=K0+K2.
(ii) If Δ1>0, i.e., E<r(K0−K)24K0Kq1, then system (2.2) has two distinct boundary equilibria ˜E2,3(˜P2,3,0), here ˜P2,3=K0+K2±12√(K0+K)2−4K0K(r+q1E)r.
For positive equilibria, from the second equation of (3.1), we have
A1P2+A2P+A3=0, | (3.2) |
where A1=β2−θ−d−q2E, A2=β2(a2−m)+θ(m−a1)+(d+q2E)(m−a1−a2),A3=a2(m−a1)(d+q2E)−β2ma2.
Let Δ2=A22−4A1A3 and h(P)=A1P2+A2P+A3. From the first equation of (3.1), we have
Z=B1P4+B2P3+B3P2+B4PK0Kβ1(P−m), |
where B1=−r, B2=r(m−a1+K0+K), B3=−[r(K0+K)(m−a1)+K0K(r+q1E)], B4=K0K(r+q1E)(m−a1).
Since the signs of A1, A2 and A3 are uncertain, there are many cases where h(P)=0 has positive root(s). For the sake of discussion, let A2>0, i.e., β2<θ(m−a1)+(d+q2E)(m−a1−a2)m−a2. Furthermore, as long as
m−a1−a2>0. Thus, when A2>0, h(P)=0 will have possible positive root(s).
Through simple analyses, we obtain the following results about the existence of the positive equilibria.
Theorem 3.2. Under the assumption that A2>0 holds, the possible positive equilibria of system (2.2) are as follows.
(i) System (2.2) has one positive equilibrium when one of the following nine conditions is satisfied
(a)A3>0,A1>0,Δ2=0,h(m)>0,h(K)>0;
(b)A3>0,A1>0,Δ2>0,h(m)<0,h(K)>0;
(c)A3>0,A1>0,Δ2>0,h(m)>0,h(K)<0;
(d)A3>0,A1<0,Δ2>0,h(m)>0,h(K)<0;
(e)A3<0,A1=0;
(f)A3<0,A1>0,Δ2>0,h(m)<0,h(K)>0;
(g)A3<0,A1<0,Δ2=0,h(m)<0,h(K)<0;
(h)A3<0,A1<0,Δ2>0,h(m)>0,h(K)<0;
(j)A3<0,A1<0,Δ2>0,h(m)<0,h(K)>0.
(ii) System (2.2) has two distinct positive equilibria when one of the following two conditions is satisfied
(k)A3>0,A1>0,Δ2>0,h(m)>0,h(K)>0;
(l)A3<0,A1<0,Δ2>0,h(m)<0,h(K)<0.
In the case of (e) in Theorem 3.2, that is, when β2=θ+d+q2E and (m−a1)(d+q2E)m<β2<θ(m−a1)+(d+q2E)(m−a1−a2)m−a2 simultaneously hold, system (2.2) has a positive equilibrium E∗(P∗,Z∗), where
P∗=−A3A2,Z∗=B1P∗4+B2P∗3+B3P∗2+B4P∗K0Kβ1(P∗−m). |
Remark 1. Due to the density of phytoplankton in system (2.2) is greater than the refuge constant m at any time, so the existence of other possible positive equilibria of system (2.2) is complex, we will not list them here. Therefore, we will focus our discussion about system (2.2) at positive equilibrium E∗(P∗,Z∗).
When τ=0, we only discuss the local stability of the boundary equilibrium ˜E1(˜P1,0) and can use the similar methods to obtain the local stability of the other boundary equilibria.
Theorem 3.3. The boundary equilibrium ˜E1(˜P1,0) of system (2.2) is locally asymptotically stable if and only if (H1) holds, where
(H1):γ1=K0+K−2m>0,r(K0+K)2>4K0K(r+q1E+d+q2E).
Proof. The Jacobian matrix of system (2.2) at the boundary equilibrium ˜E1(˜P1,0) is given by
J˜E1=(−3r˜P21+2r(K0+K)˜P1K0K−(r+q1E)−β1(˜P1−m)a1+˜P1−m0β2(˜P1−m)a1+˜P1−m−θ˜P1a2+˜P1−(d+q2E)). |
Thus, we can get
det(J˜E1)=C1+C2+C3+C4+C5+C64K0K(2a1+γ1)(2a2+K0+K),tr(J˜E1)=D1+D2+D3+D44K0K(2a1+γ1)(2a2+K0+K), |
where
γ1=K0+K−2m,C1=−r(K0+K)2(d+q2E)(2a1+γ1)(2a2+K0+K),C2=β2rγ1(K0+K)2(2a2+K0+K),C3=−rθ(K0+K)3(2a1+γ1),C4=4K0K(r+q1E)(d+q2E)(2a1+γ1)(2a2+K0+K),C5=−4K0Kβ2γ1(r+q1E)(2a2+K0+K),C6=4K0Kθ(K0+K)(r+q1E)(2a1+γ1),D1=r(K0+K)2(2a1+γ1)(2a2+K0+K),D2=4K0Kβ2γ1(2a2+K0+K),D3=−4K0Kθ(K0+K)(2a1+γ1),D4=−4K0K(2a1+γ1)(2a2+K0+K)(r+q1E+d+q2E). |
From the above analysis, it is easy to know that if and only if (H1) holds, we have tr(J˜E1)<0 and det(J˜E1)>0. Hence, the boundary equilibrium ˜E1(˜P1,0) of system (2.2) is locally asymptotically stable if and only if (H1) holds. This completes the proof.
Here, we discuss the local stability of system (2.2) at the positive equilibrium E∗(P∗,Z∗) when τ=0.
Theorem 3.4. If (H2) and (H3) simultaneously hold, then the positive equilibrium E∗(P∗,Z∗) of system (2.2) is locally asymptotically stable, where
(H2):N1+N2+N3+N4+N5+N6L3<q1<M1+M2+M3+M4+M5+M6+M7M8L1+L2,
(H3):γ2=3P∗−2(K0+K)>0.
Proof. The Jacobian matrix of system (2.2) at the positive equilibrium E∗(P∗,Z∗) is given by
JE∗=(m110m101m210m201), |
where
m110=−3rP∗2+2r(K0+K)P∗K0K−β1a1Z∗(a1+P∗−m)2−(r+q1E),m101=−β1(P∗−m)a1+P∗−m,m210=β2a1Z∗(a1+P∗−m)2−θa2Z∗(a2+P∗)2,m201=β2(P∗−m)a1+P∗−m−θP∗a2+P∗−(d+q2E). |
Then characteristic equation of system (2.2) around E∗(P∗,Z∗) is
λ2−tr(JE∗)λ+det(JE∗)=0, | (3.3) |
where
det(JE∗)=m110m201−m101m210=M1+M2+M3+M4+M5+M6+M7M8−q1(L1+L2)K0K(a2+P∗)2(a1+P∗−m)2,tr(JE∗)=m110+m201=N1+N2+N3+N4+N5+N6−q1L3K0K(P∗−m)(a2+P∗)(a1+P∗−m)2, |
here
γ2=3P∗−2(K0+K),L1=K0KE(P∗−m)(a2+P∗)(a1+P∗−m)[β2(P∗−m)(a2+P∗)−θP∗(a1+P∗−m)],L2=K0KE[M7P∗(P∗+a1−m)−(P∗−m)(d+q2E)(a2+P∗)2(a1+P∗−m)2],L3=K0KE(a2+P∗)[(P∗−m)(a1+P∗−m)2+a1P∗(m−a1−P∗)],M1=γ2rP∗(P∗−m)(d+q2E)(a2+P∗)2(a1+P∗−m)2,M2=γ2rθP∗2(P∗−m)(a2+P∗)(a1+P∗−m)2,M3=−γ2rβ2P∗(P∗−m)2(a2+P∗)2(a1+P∗−m),M4=−K0Krβ2(P∗−m)2(a1+P∗−m)(a2+P∗)2,M5=K0Kr(P∗−m)(d+q2E)(a2+P∗)2(a1+P∗−m)2,M6=K0KrθP∗(P∗−m)(a2+P∗)(a1+P∗−m)2,M7=a1θP∗(a2+P∗)+a1(d+q2E)(a2+P∗)2−θa2(P∗−m)(a1+P∗−m),M8=−rP∗4+[r(m−a1+K0+K)]P∗3−[r(K0+K)(m−a1)+K0Kr]P∗2+K0KrP∗(m−a1),N1=−γ2rP∗(P∗−m)(a2+P∗)(a1+P∗−m)2,N2=−K0K(P∗−m)(a2+P∗)(a1+P∗−m)2(r+d+q2E),N3=K0Kβ2(P∗−m)2(a2+P∗)(a1+P∗−m)N4=−K0KθP∗(P∗−m)(a1+P∗−m)2,N5=a1r(a2+P∗){P∗4−(m−a1+K0+K)P∗3+[(K0+K)(m−a1)+K0K]P∗2},N6=−a1K0Kr(a2+P∗)(m−a1)P∗. |
Through analysis and calculation, we can obtain tr(JE∗)<0 and det(JE∗)>0 if and only if (H2) and (H3) hold. Thus, we easily get that the positive equilibrium E∗(P∗,Z∗) of system (2.2) is locally asymptotically stable when (H2) and (H3) hold. This completes the proof.
In this section, we investigate the existence of Hopf bifurcation around E∗(P∗,Z∗) of system (2.2). Taking prey refuge m as the bifurcation parameter, the critical value of Hopf bifurcation is a positive root of tr(JE∗)=0, thus m=mH which satisfies det(JE∗)m=mH>0. As we know, when the value of m exceeds its critical value m=mH, the stability of the positive equilibrium E∗(P∗,Z∗) will be changed (Figure 1).
In addition, we verify the transversality condition under which Hopf bifurcation occurs. By some simple calculations, we have that ddmtr(JE∗)m=mH≠0, which implies that the stability of the positive equilibrium E∗(P∗,Z∗) changes when the parametric restriction tr(JE∗)=0 and the transversality condition are satisfied simultaneously.
Therefore, we draw the following conclusion about the occurrence of Hopf bifurcation at the positive equilibrium E∗(P∗,Z∗).
Theorem 4.1. Under the condition that the positive equilibrium E∗(P∗,Z∗) exists, the stability of system (2.2) at the positive equilibrium E∗(P∗,Z∗) is changed through critical value m=mH.
In order to facilitate the discussion of the direction of Hopf bifurcation, we use the related theory of [42] to calculate the first Lyapunov number l1 at the positive equilibrium E∗(P∗,Z∗) of system (2.2).
We transform the positive equilibrium E∗(P∗,Z∗) of system (2.2) to the origin by translation ˉP=P−P∗ and ˉZ=Z−Z∗. Then, system (2.2) in a neighborhood of the origin can be obtained
{˙ˉP=m110ˉP+m101ˉZ+m120ˉP2+m111ˉPˉZ+m130ˉP3+m121ˉP2ˉZ+R(ˉP,ˉZ),˙ˉZ=m210ˉP+m201ˉZ+m220ˉP2+m211ˉPˉZ+m230ˉP3+m221ˉP2ˉZ+S(ˉP,ˉZ), | (4.1) |
where m110, m101, m210 and m201 are the elements of the Jacobian matrix at the equilibrium E∗(P∗,Z∗). So system (2.2) experiences the Hopf bifurcation at the equilibrium E∗(P∗,Z∗), we have m110+m201=0 and Δ=det(JE∗)=m110m201−m101m210>0. The coefficients mkij(k=1,2) are given by
m120=(K0+K)r−3rP∗K0K+β1a1Z∗(a1+P∗−m)3,m111=−β1a1(a1+P∗−m)2,m130=−[rK0K+β1a1Z∗(a1+P∗−m)4],m121=β1a1(a1+P∗−m)3,m220=−β2a1Z∗(a1+P∗−m)3+θa2Z∗(a2+P∗)3,m211=β2a1(a1+P∗−m)2−θa2(a2+P∗)2,m230=β2a1Z∗(a1+P∗−m)4−θa2Z∗(a2+P∗)4,m221=−2β2a13(a1+P∗−m)3+2θa23(a2+P∗)3, |
and R(ˉP,ˉZ) and S(ˉP,ˉZ) are power series in (ˉP,ˉZ) with terms ˉPi, ˉZj satisfying i+j≥4.
The calculation formula of the first Lyapunov number l1 [42] determining the stability of the limit cycle in the planar system is described by
l1=−3Π2m101Δ32{[m110m210(m2111+m111m202+m102m211)+m110m101(m2211+m120m211+m111m202)+m2210(m111m102+2m102m202)−2m110m210(m2202−m120m102)−2m110m101(m2120−m220m202)−m2101(2m120m220+m211m220)+(m101m210−2m2110)(m211m202−m111m120)]−(m2110+m101m210)[3(m210m203−m101m130)+2m110(m121+m212)+(m210m112−m101m221)]}=−3Π2m101Δ32{[m110m210m2111+m110m101(m2211+m120m211)−2m110m101m2120−m2101(2m120m220+m211m220)−m111m120(m101m210−2m2110)]−(m2110+m101m210)[−3m101m130+2m110m121−m101m221]}. |
Based on the above analyses, we conclude the following conclusion about the direction of the limit cycle.
Theorem 4.2. If l1<0, then Hopf bifurcation is supercritical. If l1>0, then Hopf bifurcation is subcritical.
In this section, we will discuss the effect of maturation period of zooplankton τ on the dynamical behavior of system (2.1).
The local stability of the positive equilibrium E∗(P∗,Z∗) and the existence of Hopf bifurcation are studied by considering the maturity delay τ as bifurcation parameter. First, system (2.1) is linearized at positive equilibrium E∗(P∗,Z∗), we obtain
{P′=a11P(t)+a12Z(t),Z′=a21P(t)+a22Z(t)+b21P(t−τ), | (4.2) |
where
a11=−3rP∗2+2r(K0+K)P∗K0K−β1a1Z∗(a1+P∗−m)2−(r+q1E),a12=−β1(P∗−m)a1+P∗−m,a21=−θa2Z∗(a2+P∗)2,a22=β2(P∗−m)a1+P∗−m−θP∗a2+P∗−(d+q2E),b21=β2a1Z∗(a1+P∗−m)2. |
It is easy to see that corresponding characteristic equation of system (4.2) at positive equilibrium E∗(P∗,Z∗) is
f(λ,τ)=λ2+Aλ+B+Ce−λτ=0, | (4.3) |
where A=−a11−a22,B=a11a22−a12a21,C=−a12b21.
When τ=0, Eq (4.3) becomes
f(λ,0)=λ2+Aλ+B+C=0, | (4.4) |
then Eq (4.4) is equivalent to Eq (3.3). Hence, the positive equilibrium E∗(P∗,Z∗) is locally asymptotically stable when (H2) and (H3) hold.
When τ≠0, in order to get the existence of Hopf bifurcation, let λ=iω0(ω0>0) be a root of Eq (4.3) and substitute it into Eq (4.3), then we have
−ω20+B+Ccosω0τ+i(Aω0−Csinω0τ)=0. |
By separating the real and imaginary parts, we obtain
{−ω20+B+Ccosω0τ=0,Aω0−Csinω0τ=0. | (4.5) |
From which, we have
ω40−(2B−A2)ω20+B2−C2=0. | (4.6) |
Let u=ω20, then Eq (4.6) deduces to
u2−(2B−A2)u+B2−C2=0. | (4.7) |
Record Eq (4.7) as f(u)=u2−(2B−A2)u+B2−C2. From Eq (4.6), it follows that if
(H4):2B−A2>0,B2−C2>0,andA4−2A2B+4C2=0
holds, then Eq (4.6) has a unique positive root ω20. Substituting ω20 into Eq (4.5), we obtain
τ2n=1ω0arccos[ω20−BC]+2nπω0,n=0,1,2⋅⋅⋅. |
If
(H5):2B−A2>0,B2−C2>0,and(2B−A2)2>4(B2−C2)
holds, then Eq (4.6) has two different positive roots ω2+ and ω2−. We substitute ω2± into Eq (4.5), then
τ2k=1ω±arccos[ω20−BC]+2kπω±,k=0,1,2⋅⋅⋅. |
Let λ be the root of Eq (4.3) satisfying Reλ(τ2n)=0 (rep.Reλ(τ±2k)=0) and Imλ(τ2n)=ω0 (rep.Imλ(τ±2k)=ω±). Then, when τ0=min{τ2n,τ2k},n,k=0,1,2⋅⋅⋅, we have the following conclusion.
Theorem 4.3. If f′(u)>0, since dRe(λ)dτ|τ=τ0 and f′(u) have the same sign, then dRe(λ)dτ|τ=τ0>0 holds.
Proof. From Eq (4.3), we can obtain
dλdτ=Cλe−λτ2λ+A−Cτe−λτ. |
Notice that Ce−λτ=−(λ2+Aλ+B). Thus, we obtain that
(dλdτ)−1=2λ+A−λ(λ2+Aλ+B)−τλ. | (4.8) |
Substituting λ=iω0 into (4.8), we have
Re(dλdτ)−1τ=τ0=2ω40−(2B−A2)ω20(Aω20)2+(ω30−Bω0)2=ω20f′(ω20)(Aω20)2+(ω30−Bω0)2. |
Therefore,
sign{dRe(λ)dτ|τ=τ0}=sign{Re(dλdτ)−1τ=τ0}=sign{f′(ω20)}. |
If f′(ω20)≠0, we have dRe(λ)dτ|τ=τ0≠0, then dRe(λ)dτ|τ=τ0>0. If dRe(λ)dτ|τ=τ0<0, then Eq (4.3) has the roots of the real part when τ<τ0. This contradicts that the positive equilibrium E∗(P∗,Z∗) is locally asymptotically stable when τ∈(0,τ0).
According to the Hopf bifurcation theorem given in [43], we can get the following result on stability and bifurcation of system (2.1).
Theorem 4.4. For system (2.1), suppose (H4) and (H5) are satisfied. Then the positive equilibrium E∗(P∗,Z∗) is locally asymptotically stable when τ∈(0,τ0), and system (2.1) undergoes a Hopf bifurcation at E∗(P∗,Z∗) when τ passes through its critical value τ=τ0.
In the previous section, we have already obtained sufficient conditions to guarantee that system (2.1) has a periodic solution at positive equilibrium E∗(P∗,Z∗) when the critical value τ=τ0. In this section, we will use the normal form theory and the center manifold theorem presented in Hassard et al. [43] to obtain the properties of the bifurcating periodic solution.
Let τ=τ0+μ, μ∈R. Then μ=0 is a Hopf bifurcation value of system (2.1). Without loss of generality, we can choose the phase space as C=C([−τ,0],Θ). Let u(t)=(u1(t),u2(t))T∈Θ, ut(θ)=u(t+θ)∈C, system (2.1) can be rewritten as
˙u(t)=Lμ(ut)+f(μ,ut), | (4.9) |
where Lμ:C→Θ and f:R×C→Θ are defined by
Lμ(φ)=G1φ(0)+G2φ(−τ), |
G(1)=(a11a12a21a22),G(2)=(00b210),f(μ,φ)=(f1(μ,φ)f2(μ,φ)), |
where
f1(μ,φ)=ρ1φ1(0)φ2(0)+ρ2φ21(0),f2(μ,φ)=σ1φ1(0)φ2(0)+σ2φ21(0)+σ3φ21(−1)+σ4φ1(−1)φ2(0), |
here
ρ1=−β1a1(a1+P∗−m)2,ρ2=2r(K0+K)−6rP∗K0K+2β1a1Z∗(a1+P∗−m)3,σ1=−θa2(a2+P∗)2,σ2=2θa2Z∗(a2+P∗)3,σ3=−2β2a1Z∗(a1+P∗−m)3,σ4=β2a1(a1+P∗−m)2. |
Based to the Reisz representation theorem, there is a bounded variation function η(∙,μ):[−τ,0]→Θ2×2 such that
Lμ(φ)=∫0−τdη(θ,μ)φ(θ),φ∈C. |
Then, we have a choice that
dη(θ,μ)=(a11a12a21a22)δ(θ)dθ+(00b210)δ(θ+τ)dθ, |
where δ is the Dirac function. For φ∈C1([−τ,0],Θ2), we define the operator A(μ) as
A(μ)φ(θ)={dφ(θ)dθ,θ∈[−τ,0),∫0−τdη(ξ,μ)φ(ξ),θ=0. | (4.10) |
Further, we can define the operator R(μ) as
R(μ)φ(θ)={0,θ∈[−τ,0),f(φ,μ),θ=0. |
Then, we can rewrite Eq (4.9) as the following operator equation
.u(t)=Aμut+Rμut. | (4.11) |
For ψ∈C1([−τ,0],Θ2), the adjoint operator A∗0 of A0 can be defined as
A∗0ψ(s)={−dψ(s)ds,s∈(0,τ],∫0−τdηT(t,0)ψ(−t),s=0, |
where ηT is the transpose of η, and the domains of A0 and A∗0 are C1([−τ,0],Θ2) and C1([0,τ],Θ2), respectively.
For φ∈C1([−τ,0],Θ2) and ψ∈C1([0,τ],Θ2), a bilinear form is given by
⟨ψ,φ⟩=ˉψ(0)φ(0)−∫0−τ∫θξ=0ˉψ(ξ−θ)dη(θ)φ(ξ)dξ, | (4.12) |
where η(θ)=η(θ,0). Then A0 and A∗0 are a pair of adjoint operators. By the results obtained in the last section, we know that ±iω0 are the eigenvalues of A0. Thus, they are also eigenvalues of A∗0. What's more, we assume that q(θ) is the the eigenvalues of A0 corresponding to iω0, then
A0q(θ)=iω0q(θ). | (4.13) |
By Eq (4.10), Eq (4.13) becomes
{dq(θ)dθ=iω0q(θ),θ∈[−τ,0],L0q(0)=iω0q(0),θ=0. | (4.14) |
From Eq (4.14) we can get
q(θ)=Veiω0θ,θ∈[−τ,0], | (4.15) |
where V=(v1,v2)T∈Θ2 is a constant vector. By virtue of Eq (4.15), we may obtain
G1V+G2e−iω0τV−iω0IV=0, |
which yields
V=(v1v2)=(1iω0−a11a12). |
On the other hand, if −iω0 is the eigenvalue of A∗0, then we have
A∗0q∗(ξ)=iω0q∗(ξ). |
For the non-zero vector q∗(ξ), ξ∈[0,τ0], we can obtain
GT1V∗+GT2eiω0τV∗+iω0IV∗=0. |
Let q∗(ξ)=DV∗eiω0ξ, where ξ∈[0,τ0], and V∗=(v∗1,v∗2)T be a constant vector. Similarly, we get
V∗=(v∗1v∗2)=(−iω0+a22a121). |
In fact, by Eq (4.12), we have
⟨q∗,q⟩=¯q∗T(0)q(0)−∫0−τ0∫θξ=0¯q∗T(ξ−θ)dη(θ)q(ξ)dξ,=ˉD[¯V∗TV−∫0−τ0∫θξ=0¯V∗Te−iω0(ξ−θ)dη(θ)Veiω0ξdξ]=ˉD[¯V∗TV+τ0e−iω0τ0¯V∗TG2V]=ˉD[v2+¯v∗1+τ0b21e−iω0τ0]. | (4.16) |
Actually, we can choose
ˉD=[v2+¯v∗1+τ0b21e−iω0τ0]−1. |
From Eq (4.16) we obtain that ⟨q∗,q⟩=1. More importantly
−iω0⟨q∗,ˉq⟩=⟨q∗,A0ˉq⟩=⟨A∗0q∗,ˉq⟩=⟨−iω0q∗,ˉq⟩=iω0⟨q∗,ˉq⟩, |
thus, ⟨q∗,ˉq⟩=0 is easily verified.
Based on the above analysis, we can come to the following simple conclusion.
Theorem 4.5. Let q(θ)=Veiω0θ, where θ∈[−τ,0], is the eigenvector of A0 corresponding to iω0, and let q∗(s)=DV∗eiω0s, where s∈[0,τ] is the eigenvector of A∗0 corresponding to −iω0. Then
⟨q∗,q⟩=1,⟨q∗,ˉq⟩=0,V=(1,v2)T,V∗=(v∗1,1)T, |
where
v2=iω0−a11a12,v∗1=−iω0+a22a12,ˉD=[v2+¯v∗1+τ0b21e−iω0τ0]−1. |
Next, we use the same notations as the previous part to study the stability of bifurcating periodic solution. We shall calculate the coordinates to express the center manifold C0 at μ=0. Let ut be the solution of .u(t)=Aμut+Rμut at μ=0 and define
z(t)=⟨q∗,ut⟩, | (4.17) |
W(t,θ)=ut(θ)−2Re[z(t)q(θ)]. | (4.18) |
On the center manifold C0, we have
W(t,θ)=W[z(t),ˉz(t),θ], |
and
W[z,ˉz,θ]=W20(θ)z22+W11(θ)zˉz+W02(θ)ˉz22+W30(θ)z36+⋅⋅⋅, |
where z and ˉz are the local coordinates for the center manifold C0 in the direction of q∗ and ¯q∗. Notice that W is real if ut is real. So here we only consider the real solutions.
For the solution ut∈C0 of Eq (4.11), we have
˙z(t)=⟨q∗,˙ut⟩=⟨q∗,A0ut+R0ut⟩=⟨A∗0q∗,ut⟩+¯q∗(0)f0(0,ut)=iω0z+¯q∗T(0)f(z,ˉz). | (4.19) |
Then Eq (4.19) can be rewritten as
˙z(t)=iω0z+g(z,ˉz), | (4.20) |
where
g(z,ˉz)=g20(θ)z22+g11(θ)zˉz+g02(θ)ˉz22+g30(θ)z36+⋅⋅⋅. | (4.21) |
According to Eqs (4.11), (4.17) and (4.18), we may obtain
˙W=˙ut−˙zq−˙ˉzˉq={A0(W)−2Re[ˉq∗f0q(θ)],θ∈[−τ,0),A0(W)−2Re[ˉq∗f0q(0)]+f0,θ=0. | (4.22) |
We can rewrite Eq (4.22) as
˙W=A0(W)+H(z,ˉz,θ), | (4.23) |
where
H(z,ˉz,θ)=H20(θ)z22+H11(θ)zˉz+H02(θ)ˉz22+H30(θ)z36+⋅⋅⋅. | (4.24) |
Substituting the corresponding series into Eq (4.22) and comparing the coefficients, we have
(A0−2iω0I)W20(θ)=−H20(θ),A0W11(θ)=−H11(θ),(A0+2iω0I)W02(θ)=−H02(θ). | (4.25) |
According to Eqs (4.19) and (4.20), we know
g(z,ˉz)=ˉq∗(0)f(0)(W(z,ˉz,0)+2Re(z(t)q(θ)))=ˉD(ˉv∗1,1)(ρ1φ1(0)φ2(0)+ρ2φ21(0)σ1φ1(0)φ2(0)+σ2φ21(0)+σ3φ21(−1)+σ4φ1(−1)φ2(0))=ˉD((ˉv∗1ρ1+σ1)(W(1)20(0)z22+W(1)11(0)zˉz+W(1)02(0)ˉz22+v1z+ˉv1ˉz)×(W(2)20(0)z22+W(2)11(0)zˉz+W(2)02(0)ˉz22+z+ˉz)+(ˉv∗1ρ2+σ2)×(W(1)20(0)z22+W(1)11(0)zˉz+W(1)02(0)ˉz22+v1z+¯v1ˉz)2+σ3(W(1)20(−1)z22+W(1)11(−1)zˉz+W(1)02(−1)ˉz22+v1ze−iω0τ0+¯v1ˉzeiω0τ0)2+σ4(W(1)20(−1)z22+W(1)11(−1)zˉz+W(1)02(−1)ˉz22+v1ze−iω0τ0+¯v1ˉzeiω0τ0)×(W(2)20(0)z22+W(2)11(0)zˉz+W(2)02(0)ˉz22+z+ˉz)). |
Comparing the coefficients with Eq (4.21), it follows that
g20=2ˉD[ˉv∗1K11+K21],g11=ˉD[ˉv∗1K12+K22],g02=2ˉD[ˉv∗1K13+K23],g21=2ˉD[ˉv∗1K14+K24], |
where
K11=ρ1v1+ρ2v21,K12=ρ1(v1+ˉv1)+2ρ2v1ˉv1,K21=σ1v1+σ2v21+σ3v21e−2iω0τ0+σ4v1e−iω0τ0,K22=σ4(v1e−iω0τ0+ˉv1eiω0τ0)+σ1(v1+ˉv1)+2σ2v1ˉv1+2σ3v1ˉv1,K13=ρ1ˉv1+ρ2ˉv21,K23=σ1ˉv1+σ2ˉv21+σ3ˉv21e2iω0τ0+σ4v1eiω0τ0,K14=ρ1(12W(1)20(0)+W(1)11(0)+12W(2)20(0)ˉv1+W(2)11(0)v1)+2ρ2(12W(1)20(0)ˉv1+W(1)11(0)v1),K24=σ1(12W(1)20(0)+W(1)11(0)+12W(2)20(0)ˉv1+W(2)11(0)v1)+2σ2(12W(1)20(0)ˉv1+W(1)11(0)v1)+2σ3(12W(1)20(−1)ˉv1eiω0τ0+W(1)11(−1)v1e−iω0τ0)+σ4(12W(1)20(−1)+W(1)11(−1)+12W(2)20(0)ˉv1eiω0τ0+W(2)11(0)v1e−iω0τ0). |
Since W20(θ) and W11(θ) appear in g21, we need to compute them further. From Eqs (4.22) and (4.23), we have that for θ∈[−τ,0),
H(z,ˉz,θ)=−2Re[¯q∗(0)f0(z,ˉz)q(θ)]=−2Re[g(z,ˉz)q(θ)]=−g(z,ˉz)q(θ)−ˉg(z,ˉz)ˉq(θ). |
Comparing the coefficients with Eq (4.24) gives that
H20(θ)=−g20q(θ)−ˉg20ˉq(θ),H11(θ)=−g11q(θ)−ˉg11ˉq(θ). |
From Eqs (4.10) and (4.25), we have
˙W20(θ)=2iω0W20(θ)+g20q(θ)+ˉg02ˉq(θ), | (4.26) |
and
˙W11(θ)=g11q(θ)+ˉg11ˉq(θ). | (4.27) |
Solving for W20(θ) and W11(θ), we obtain
W20(θ)=ig20q(0)ω0eiω0θ+iˉg02ˉq(0)3ω0e−iω0θ+E1e2iω0θ, | (4.28) |
and
W11(θ)=−ig11q(0)ω0eiω0θ+iˉg11ˉq(0)ω0e−iω0θ+E2, | (4.29) |
where E1=(E(1)1,E(2)1) and E2=(E(1)2,E(2)2) are the following two-dimensional constant vectors, and can be determined by setting θ=0 in H.
In fact, the following formula is true at θ=0,
H(z,ˉz,0)=−2Re[¯q∗(0)f0(z,ˉz)q(0)]+f0, |
we have
H20(0)=−g20q(0)−ˉg02ˉq(0)+(K11K21), | (4.30) |
and
H11(0)=−g11q(0)−ˉg11ˉq(0)+(K12K22). | (4.31) |
Substituting Eqs (4.28) and (4.29) into Eqs (4.26) and (4.27), respectively, we get
(a11a12a21a22)W20(0)+(00b210)W20(−τ)=2iω0W20(0)−H20(0), | (4.32) |
and
(a11a12a21a22)W11(0)+(00b210)W11(−τ)=−H11(0). | (4.33) |
Substituting Eq (4.28) into Eq (4.32), we have
(a11a12a21a22)[ig20ω0(1v2)+iˉg023ω0(1ˉv2)+E1]+(00b210)[ig20ω0(1v2)e−iω0τ+iˉg023ω0(1ˉv2)eiω0τ+E1e−2iω0τ]=−2iω0[ig20ω0(1v2)+iˉg023ω0(1ˉv2)+E1]−H20. | (4.34) |
Since iω0 is the eigenvalue of A0 and q(0) is the corresponding eigenvector, we get
(iω0I−∫0−τ0eiω0θdη(θ))q(0)=0,(−iω0I−∫0−τ0e−iω0θdη(θ))ˉq(0)=0, |
that is
(a11a12a21a22)(1v2)+(00b210)(1v2)e−iω0τ=iω0(1v2), | (4.35) |
and
(a11a12a21a22)(1ˉv2)+(00b210)(1ˉv2)eiω0τ=iω0(1ˉv2). | (4.36) |
By Eqs (4.30) and (4.34)–(4.36), we obtain
(2iω0−a11−a12−a21−b21e−2iω0τ2iω0−a22)E1=(K11K21), |
which leads to
E(1)1=M(1)1M1,E(2)1=M(2)1M1, |
where
M1=(2iω0−a11)(2iω0−a22)−a12(a21+b21e−2iω0τ),M(1)1=K11(2iω0−a22)+K21a12,M(2)1=K21(2iω0−a11)+K11(a21+b21e−2iω0τ). |
Similarly, substituting Eqs (4.29) and (4.31) into Eq (4.33), we have
(−a11−a12−a21−b21−a22)E1=(K12K22), |
which leads to
E(1)2=M(1)2M2,E(2)2=M(2)2M2, |
where
M2=a11a22−a12(a21+b21),M(1)2=−K12a22+K22a12,M(2)2=−K22a11+K12(a21+b21). |
Through the above calculation and analysis, it is not difficult to see that each gij depends on the parameters and time delay of system (2.1). Thus, we can obtain the following expressions
{C1(0)=i2ω0(g20g11−2|g11|2−|g02|23)+g212,μ2=−Re{C1(0)}Re{λ′(τ0)},β=2Re{C1(0)},T2=−Im{C1(0)}+μ2Im{λ′(τ0)}ω0, | (4.37) |
which determine the direction of Hopf bifurcation and stability of bifurcating periodic solutions of system (2.1) on the center manifold at the critical values τ0. From the conclusion of Hassard et al. [43], we summarize the following main findings.
Theorem 4.6. The values of the parameters μ2, β and T2 of (4.37) will determine the properties of Hopf bifurcation of system (2.1).
(i) The sign of μ2 determines the direction of Hopf bifurcation: The Hopf bifurcation is supercritical if μ2>0 and the Hopf bifurcation is subcritical if μ2<0.
(ii) The sign of β determines the stability of bifurcating periodic solutions: The periodic solutions are stable if β<0 and unstable if β>0.
(iii) The sign of T2 determines the period of bifurcating periodic solutions: The period increases if T2>0 and the period decreases if T2<0.
When τ=0, let ˆc be the constant harvesting cost per unit effort and p1, p2 are the constant price per unit biomass of phytoplankton and zooplankton, respectively. Then the net economic revenue to the society is given by
π(P,Z,E,t)=(p1q1P+p2q2Z−ˆc)E. |
In order to study the optimal harvesting yield of system (2.2), we will consequently maximize the full return from resource management. Hence we take the harvesting effort E as a control variable and consider a objective function defined by the present value J of a continuous time-stream of revenues
J=∫∞0L(X,E,t)dt, | (5.1) |
where
L(X,E,t)=e−δt(p1q1P+p2q2Z−ˆc)E, |
where δ is the instantaneous annual discount rate. We consider that the present value of capital flow over time depends on discount rate δ. Here X=[P,Z]T is the vector of state variables, it can be written as ˙X=f(X,E), X(0)=X0 by according to the state equations of (2.2).
Our optimal control problem is to maximize Eq (5.1) subject to the state equations of (2.2) and to the control constraint
0≤E(t)≤Emax, |
where Emax is the maximum harvesting effort.
Based on the Pontryagin's Maximum Principle [44], we can obtain the optimal solution of this problem. We know that the convexity of objective function with respect to E(t), the linearity of the control differential equations and the compactness of the range values of the state variables to ensure the existence of optimal control.
The present value Hamiltonian function of the optimal problem is formulated by
H(X,E,t)=L(X,E,t)+λT(t)f(X,E), |
where λ(t)=[λ1(t),λ2(t)]T is the vector of constant or adjoint variables.
Next, we substitute λT(t) and f(X,E) into above Hamiltonian function. Then, the Hamiltonian function becomes
H(P,Z,E,t)=e−δt(p1q1P+p2q2Z−ˆc)E+λ1(t)[rP(1−PK)(PK0−1)−β1(P−m)a1+(P−m)Z−q1EP]+λ2(t)[β2(P−m)a1+(P−m)Z−dZ−θPa2+PZ−q2EZ]. |
To make H reach the maximum on the control set 0≤E(t)≤Emax, the condition that the Hamiltonian function H must satisfy is presented by
∂H∂E=e−δt(p1q1P+p2q2Z−ˆc)−λ1(t)q1P−λ2(t)q2Z=0. | (5.2) |
Pontryagin's Maximum Principle [44] states that the optimal state trajectory, optimal control, and corresponding adjoint variable vector must satisfy the following adjoint equations
−˙λ(t)=HX. |
Obviously, the adjoint equations are
−dλ1dt=∂H∂P=e−δtp1q1E+λ1(t)[−3rP2+2(K0+K)rPK0K−β1a1Z(a1+P−m)2−r−q1E]+λ2(t)[β2a1Z(a1+P−m)2−θa2Z(a2+P)2],−dλ2dt=∂H∂Z=e−δtp2q2E−λ1(t)β1(P−m)a1+P−m+λ2(t)[β2(P−m)a1+P−m−θPa2+P−d−q2E]. |
For the positive optimal solutions, which satisfy ˙P=˙Z=0 (in other words, P, Z are not dependent on t) and from two equations of system (2.2), we have
r(1−PK)(PK0−1)−β1(P−m)ZP(a1+P−m)Z−q1E=0, | (5.3) |
β2(P−m)a1+P−m−d−θPa2+P−q2E=0. | (5.4) |
From the above analysis, it is obvious that E is also independent of t. Furthermore, we get
−dλ1dt=λ1(t)[−2rP2+(K0+K)rPK0K+β1Z(P−m)2−β1ma1ZP(a1+P−m)2]+λ2(t)[β2a1Z(a1+P−m)2−θa2Z(a2+P)2]+e−δtp1q1E, |
and
−dλ2dt=e−δtp2q2E−λ1(t)β1(P−m)a1+P−m. | (5.5) |
Differentiating Eq (5.2) and replacing value of ˙λ1, ˙λ2, we get
[−2rq1P3+(K0+K)rq1P2K0K+β1PZ(q1−q2)[(P−m)2−a1m]−β1q2a1P2ZP(a1+P−m)2]×λ1eδt+λ2eδt[q1β2a1PZ(a1+P−m)2−q1θa2PZ(a2+P)2]=δF−p1q21EP−p2q22EZ. | (5.6) |
By Eqs (5.2) and (5.6), we can get
λ1eδt=P1+P2Q1+Q2+Q3, | (5.7) |
λ2eδt=P3+P4+P5Q4+Q5+Q6, | (5.8) |
where
P1=K0Kq2PZ(a2+P)2(a1+P−m)2[δF−E(p1q21P+p2q22Z)],P2=−K0KFq1β2a1P2Z(a2+P)2+K0KFq1θa2P2Z(a1+P−m)2,P3=K0Kq1P2(a2+P)2(a1+P−m)2[δF−E(p1q21P+p2q22Z)],P4=−rq1FP3(a2+P)2(a1+P−m)2(K0+K−2P),P5=−β1FK0KPZ(q1−q2)(a2+P)2[(P−m)2−a1m]+FK0Kβ1q2P2Z(a2+P)2,Q1=rq1q2P3Z(K0+K−2P)(a2+P)2(a1+P−m)2,Q2=K0Kq2β1PZ2(q1−q2)(a2+P)2[(P−m)2−a1m]−K0Kβ1q22a1P2Z2(a2+P)2,Q3=−K0Kq21β2a1P3Z(a2+P)2+K0Kq21θa2P2Z(a1+P−m)2,Q4=K0Kq21P3Z[β2a1(a2+P)2−θa2(a1+P−m)2],Q5=−rq1q2P3Z(a2+P)2(a1+P−m)2(K0+K−2P),Q6=−β1K0Kq1PZ2(q1−q2)(a2+P)2[(P−m)2−a1m]+K0Kβ1q22a1P2Z2(a2+P)2. |
Now removing E from Eqs (5.3) and (5.4), we obtain
r(1−PK)(PK0−1)−β1(P−m)P(a1+P−m)Z=q1q2[β2(P−m)a1+P−m−d−θPa2+P], | (5.9) |
which is the optimal trajectory of the steady state given by the optimal solutions P=Pδ, Z=Zδ. Then, we substitute λ1 and λ2 into Eq (5.5) and obtain optimal equilibrium level of effort given by
Eδ=δλ2(a1+P−m)+λ1β1(P−m)p2q2(a1+P−m)eδt. | (5.10) |
By solving Eqs (5.9) and (5.10) when assigning a certain value to δ, we can obtain the optimal equilibrium level (Pδ,Zδ).
The optimal harvesting effort at any time is determined by
E(t)={Emin,∂H∂E<0,Eδ,∂H∂E=0,Emax,∂H∂E>0, |
where Emin is the minimum harvesting effort.
In this section, we will do some numerical simulations to verify the theoretical results. The values of all parameters in system (2.2) are sourced from Table 1. And the initial values of the system (2.2) are assumed to be P(0)=400, Z(0)=800.
Parameter | Description | Value |
r | The intrinsic growth rate of phytoplankton | 0.8 |
K | Environmental carrying capacity of phytoplankton | 500 |
β1 | Predation rate of zooplankton | 1 |
β2 | Growth efficiency of zooplankton | 0.89 |
d | Mortality rate of zooplankton | 0.2 |
a1 | Half saturation constant | 0.5 |
a2 | Half saturation constant | 9.2 |
θ | Toxin production rate | 0.39 |
m | Refuge capacity | defaulted |
K0 | Critical level of the growth of phytoplankton | 25 |
q1 | Catchability cofficient of phytoplankton | 0.03 |
q2 | Catchability cofficient of zooplankton | 0.03 |
E | Combined harvesting effort | 10 |
First, according to the case (e) in Theorem 3.2, there is a positive equilibrium E∗=(343.2,1004.4) of system (2.2). In addition, if assumptions (H2) and (H3) hold, the positive equilibrium E∗ is locally asymptotically stable (Figure 2). We can clearly see that the solution curve starting from different initial values eventually tends to the black point, that is, the positive equilibrium E∗=(343.2,1004.4) (Figure 2b).
Second, for the parameter values given above, we obtain mH=286.6 and the first Lyapunov number l1=−0.0187<0 by simple calculation. This indicates that system (2.2) has a stable limit cycle around the positive equilibrium E∗. This result is shown in Figure 3.
Next, for the given parameters, we get ω0=0.2354 and τ0=0.011 when τ≠0. According to Theorem 4.4, we obtain that the positive equilibrium E∗=(343.2,1004.4) is locally asymptotically stable when τ=0.01<τ0=0.011 (Figure 4). Then, we choose the value of τ as τ=0.1>τ0=0.011 and obtain that C1(0)=−0.0019−0.0152i, μ2=6.4946×10−21>0, β=−0.0038<0, T2=0.0065>0 by using formula (4.37). On the basis of Theorem 4.6, we know that the system (2.1) experiences a supercritical Hopf bifurcation when τ passes its critical value τ0. Other than this, system (2.1) has stable bifurcating periodic solutions and the period of the bifurcating periodic solutions is increasing. We clearly see from the Figure 5 that the positive equilibrium E∗=(343.2,1004.4) is destabilized through a Hopf bifurcation.
Finally, we consider the following parameter values: p1=1, p2=5, ˆc=0.1, δ=0.2 and the other parameters remain unchanged. Figure 6 shows the solution curve of the state variables with time. Figure 7a, b show the variation curve of the adjoint variables λ1 and λ2, respectively. It is easy to see from the Figure 7 that the adjoint variables λ1 and λ2 tend ultimately to 0 with the increase of time. In addition, the effect of the constant refuge capacity of phytoplankton population m, the critical value of the growth of phytoplankton K0, the half saturation constant a1 and the half saturation constant a2 on the optimal harvesting effort in Figure 8. It is not difficult to see that the optimal harvesting effort decreases as m increases (Figure 8a), but the optimal harvesting effort increases as K0, a1 and a2 increase (Figure 8b–d).
In this paper, we studied the dynamics of the phytoplankton-zooplankton system in which the growth of phytoplankton is affected by Allee effect and the growth of zooplankton is affected by maturation delay. For the positive equilibrium E∗, due to the expression of the trace and determinant of its Jacobian matrix are very complicated, so the stability of the positive equilibrium E∗ is verified by the combination of theoretical derivation and numerical simulation. When the maturation delay of zooplankton is not considered, the strict mathematical proof of the existence of Hopf bifurcation is given by using the relevant bifurcation theory. In addition, we derive the expression of the first Lyapunov number l1 that determines the direction of the Hopf bifurcation. Furthermore, when considering the maturation period of phytoplankton, we obtain some properties of the Hopf bifurcation through the normal form theory and the center manifold theorem. Because plankton has certain economic significance, we also consider linear harvesting for both phytoplankton and zooplankton and obtain the optimal harvesting policy by Pontryagin's Maximum Principle in this paper.
Comparing with the study on the phytoplankton-zooplankton system in [19], our model is more realistically by considering Allee effect and maturation delay into growth of phytoplankton and zooplankton, respectively. What's more, both populations are linearly harvested. For phytoplankton, we can predict the stability of system (2.2) at the positive equilibrium E∗ by selecting the parameter values to determine the critical value mH of the refuge capacity m. When we choose m>mH, system (2.2) is stable at the positive equilibrium E∗, but when m<mH, system (2.2) experiences a supercritical Hopf bifurcation and loses stability. This indicates that increasing the refuge capacity m is important for balancing ecosystem. For zooplankton, we regard the maturation delay of zooplankton as a parameter to predict the stability of system (2.1). The positive equilibrium E∗ of the system (2.1) is stable, if the maturation delay of zooplankton τ<τ0, which implies that the densities of phytoplankton and zooplankton will tend to be stable constants, indicating the ecosystem is balanced. But system (2.1) undergoes a Hopf bifurcation at the positive equilibrium E∗ if the the maturation delay of the zooplankton τ>τ0, which shows that the densities of phytoplankton and zooplankton species will oscillate periodically.
Through the above analysis, we know that if the refuge capacity m<mH of phytoplankton or the maturation delay τ>τ0 of zooplankton, the densities of phytoplankton and zooplankton will change periodically, which indicates the system becomes unstable. If the refuge capacity m>mH of phytoplankton or the maturation delay τ<τ0 of zooplankton, the densities of phytoplankton and zooplankton will tend to be stable, which suggests a state of ecosystem balance. Thus, we can maintain the ecological balance by adjusting the values of the refuge capacity m of phytoplankton and the maturation delay τ of zooplankton determined by system parameters.
The authors are grateful to the anonymous reviewers for their constructive comments. This work is supported by the National Natural Science Foundation of China (Grant Nos. 11661050 and 11861044), and the HongLiu First-class Disciplines Development Program of Lanzhou University of Technology.
All authors declare no conflicts of interest in this paper.
[1] | 10 Jahre Solarparks von Green City - Auf in die nächste Phase der Energiewende. (2013) Available from: https://www.pressebox.de/pressemitteilung/green-city-energy-ag/10-Jahre-Solarparks-von-Green-City-Auf-in-die-naechste-Phase-der-Energiewende/boxid/631572 (accessed on 08.08.2023) |
[2] | Abriss nach Windrad-Havarie - Suche nach Ursache geht weiter. (2021) Proplanta GmbH & Co. KG. Available from: https://www.proplanta.de/agrar-nachrichten/energie/abriss-nach-windrad-havarie-suche-nach-ursache-geht-weiter_article1633485787.html. (accessed on 05.02.2024) |
[3] | Ad hoc: Siemens Energy kommentiert Medienberichte. (2023) Available from: https://www.siemens-energy.com/de/de/home/pressemitteilungen/ad-hoc--siemens-energy-kommentiert-medienberichte.html. (accessed on 05.11.2023) |
[4] |
Afridi FEA, Jan S, Ayaz B, et al. (2021) Green finance incentives: An empirical study of the Pakistan banking sector. Revista Amazonia Investiga 10: 169–176. https://doi.org/10.34069/ai/2021.41.05.17 doi: 10.34069/ai/2021.41.05.17
![]() |
[5] |
Aghion P, Boneva L, Breckenfelder J, et al. (2022) Financial Markets and Green Innovation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4173682 doi: 10.2139/ssrn.4173682
![]() |
[6] |
Agliardi R (2022) Green securitisation. J Sustain Financ Inv 12: 1330–1345. https://doi.org/10.1080/20430795.2021.1874214 doi: 10.1080/20430795.2021.1874214
![]() |
[7] | Ahima T (2010) Warnung vor grünen Geldanlagen: Risikogeschäfte bei Windparks. taz Verlags u. Vertriebs GmbH. Available from: https://taz.de/Warnung-vor-gruenen-Geldanlagen/!5147325/. (accessed on 10.08.2023) |
[8] |
Akomea-Frimpong I, Adeabah D, Ofosu D, et al. (2022) A review of studies on green finance of banks, research gaps and future directions. J Sustain Financ Inv 12: 1241–1264. https://doi.org/10.1080/20430795.2020.1870202 doi: 10.1080/20430795.2020.1870202
![]() |
[9] | Amann C (2023) Welche Probleme hat Siemens Gamesa mit seinen Windturbinen? Reuters. Available from: https://www.onvista.de/news/2023/06-23-welche-probleme-hat-siemens-gamesa-mit-seinen-windturbinen-20-26147733. (accessed on 05.02.2024) |
[10] |
Andersen LB, Häger D, Maberg S, et al. (2012) The financial crisis in an operational risk management context-A review of causes and influencing factors. Reliab Eng Syst Safe 105: 3–12. https://doi.org/10.1016/j.ress.2011.09.005 doi: 10.1016/j.ress.2011.09.005
![]() |
[11] | Anleihebedingungen 2016 - 2030. PROKON Regenerative Energien eG. Available from: https://www.prokon.net/ueber-uns/investor-relations/prokon-anleihe-2016–2030. (accessed on 30.08.2023) |
[12] |
Bachner G, Mayer J, Steininger KW (2019) Costs or benefits? Assessing the economy-wide effects of the electricity sector's low carbon transition - The role of capital costs, divergent risk perceptions and premiums. Energy Strateg Rev 26: 100373. https://doi.org/10.1016/j.esr.2019.100373 doi: 10.1016/j.esr.2019.100373
![]() |
[13] | Bergermann M, Jens B, Christian K (20122) Lücken in der Ökobilanz. Capital. Available from: https://download.djp.de/downloadarticle.php?p1 = 2980 & p2 = 1341943224 & p3 = save. (accessed on 03.11.2023) |
[14] | Beschluss Bestätigung Genossenschaftsinsolvenzplan (2015) PROKON Abgeltungsgläubiger SPV GmbH. Available from: https://prokon-spv.insolvenz-solution.de/download/Beschluss_gerichtliche_Bestaetigung_eG-Insolvenzplan_PROKON.pdf. (accessed on 20.08.2023) |
[15] | Bock M, Tichy J (2016) FAILURE OF THE CONTROL MECHANISMS IN US BANKS DURING THE CRISIS AND SPREAD OF THE FINANCIAL CRISIS INTO THE WORLD THROUGH STRUCTURED PRODUCTS. Ad Alta-Journal of Interdisciplinary Research 6: 79–81. |
[16] |
Bourcet C, Bovari E (2020) Exploring citizens' decision to crowdfund renewable energy projects: Quantitative evidence from France. Energ Econ 88: 104754. https://doi.org/10.1016/j.eneco.2020.104754 doi: 10.1016/j.eneco.2020.104754
![]() |
[17] |
Campiglio E, Daumas L, Monnin P, et al. (2023) Climate‐related risks in financial assets. J Econ Surv 37: 950–992. https://doi.org/10.1111/joes.12525 doi: 10.1111/joes.12525
![]() |
[18] |
Chabot M, Bertrand JL (2023) Climate risks and financial stability: Evidence from the European financial system. J Financ Stabil 69: 101190. https://doi.org/10.1016/j.jfs.2023.101190 doi: 10.1016/j.jfs.2023.101190
![]() |
[19] |
Chen HX, Shi Y, Zhao X (2022) Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: A case of US economy. Resour Policy 77: 102680. https://doi.org/10.1016/j.resourpol.2022.102680 doi: 10.1016/j.resourpol.2022.102680
![]() |
[20] |
Chen L (2024) Unraveling the drivers of greenwashing in China's new energy sector: A PLS‐SEM and fsQCA analysis. Manag Decis Econ 45: 1528–1546. https://doi.org/10.1002/mde.4089 doi: 10.1002/mde.4089
![]() |
[21] | COMMISSION DELEGATED REGULATION (EU) 2023/2486, (2023). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri = CELEX: 32023R2486. |
[22] | Consolidated Non-Financial Statement 2022 (2023) S. G. R. E. S.A. Available from: https://www.siemensgamesa.com/en-int/-/media/siemensgamesa/downloads/en/sustainability/siemens-gamesa-consolidated-non-financial-statement-2022-en.pdf. (accessed on 03.02.2024) |
[23] |
Curcio D, Gianfrancesco I, Vioto D (2023) Climate change and financial systemic risk: Evidence from US banks and insurers. J Financ Stabil 66: 101132. https://doi.org/10.1016/j.jfs.2023.101132 doi: 10.1016/j.jfs.2023.101132
![]() |
[24] |
D'Orazio P (2021) Towards a post-pandemic policy framework to manage climate-related financial risks and resilience. Clim Policy 21: 1368–1382. https://doi.org/10.1080/14693062.2021.1975623 doi: 10.1080/14693062.2021.1975623
![]() |
[25] |
Debrah C, Darko A, Chan APC (2023) A bibliometric-qualitative literature review of green finance gap and future research directions. Clim Dev 15: 432–455. https://doi.org/10.1080/17565529.2022.2095331 doi: 10.1080/17565529.2022.2095331
![]() |
[26] | Delegated Regulation (EU) 2021/1235 amending Delegated Regulation (EU) 2017/565 as regards the integration of sustainability factors, risks and preferences into certain organisational requirements and operating conditions for investment firms. (2021) Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri = CELEX%3A32021R1253. |
[27] |
Desalegn G, Tangl A (2022) Enhancing Green Finance for Inclusive Green Growth: A Systematic Approach. Sustainability 14: 7416. https://doi.org/10.3390/su14127416 doi: 10.3390/su14127416
![]() |
[28] |
Deschryver P, De Mariz F (2020) What Future for the Green Bond Market? How Can Policymakers, Companies, and Investors Unlock the Potential of the Green Bond Market? J Risk Financ Manag 13: 61. https://doi.org/10.3390/jrfm13030061 doi: 10.3390/jrfm13030061
![]() |
[29] | Die Freunde Prokons e.V. Available from: https://fvp-ev.de/verein/Entstehung. (accessed on 31.08.2023) |
[30] | Die zehn größten Kursstürze im Dax (2023) manager magazin new media GmbH & Co. KG. Available from: https://www.manager-magazin.de/finanzen/aktien-crash-von-siemens-energy-und-co-die-zehn-groessten-kursstuerze-im-dax-a-b5816971-b21d-4300-826b-25d061889ae7. (accessed on 05.11.2023) |
[31] | Diekmann F (2014) Insolventer Ökokonzern Mitarbeiter feiern Prokon-Chef. DER SPIEGEL GmbH & Co. KG. Available from: https://www.spiegel.de/wirtschaft/unternehmen/prokon-pk-rodbertus-beklagt-medienkampagne-a-945141.html. (accessed on 01.09.2023) |
[32] | Drygala T (2014) Prokon - Kein Genuss ohne Folgen. Wolters Kluwer Deutschland GmbH. Available from: https://www.lto.de/recht/hintergruende/h/prokon-genussrechte-liquiditaet-insolvenz-kapitalmarkt/2/. (accessed on 02.02.2024) |
[33] |
Fichtner J, Jaspert R, Petry J (2024) Mind the < scp > ESG < /scp > capital allocation gap: The role of index providers, standard‐setting, and "green" indices for the creation of sustainability impact. Regul Gov 18: 479–498. https://doi.org/10.1111/rego.12530 doi: 10.1111/rego.12530
![]() |
[34] |
Frydrych S (2021) Green bonds as an instrument for financing in Europe. Ekonomia i Prawo 20: 239–255. https://doi.org/10.12775/eip.2021.014 doi: 10.12775/eip.2021.014
![]() |
[35] | Giglio S, Maggiori M, Stroebel J, et al. (2023) Four Facts About ESG Beliefs and Investor Portfolios. National Bureau of Economic Research. |
[36] | Green City AG - Informationen für Anleger*inne. GLS Gemeinschaftsbank eG. Available from: https://www.gls.de/privatkunden/anlegen-sparen/green-city-energy/. (accessed on 02.09.2023) |
[37] | Green City AG Konzernabschluss 2017. Available from: https://www.bundesanzeiger.de/pub/de/suchergebnis?6. (accessed on 30.01.2024) |
[38] | Green City AG stellt Insolvenzantrag. (2022) Available from: https://gc-ag.org/docs/Pressemitteilung-Green-City-AG-stellt-Insolvenzantrag.pdf. (accessed on 03.08.2023) |
[39] | Green City AG: Der Jahresabschluss 2018 und die Perspektiven für 2020 (2020) ECOreporter GmbH. Available from: https://www.ecoreporter.de/artikel/green-city-ag-der-jahresabschluss-2018-und-die-perspektiven-fur-2020/. (accessed on 04.09.2023) |
[40] | Green City Anleihen (2023) Finanzen.net. Available from: https://www.finanzen.net/anleihen/green-city-anleihen. (accessed on 30.01.2024) |
[41] | Green City Energy - Unseriöse Werbung für alternative Energien (2015) Stiftung Warentest. Available from: https://www.test.de/Green-City-Energy-Unserioese-Werbung-fuer-alternative-Energien-4848835-0/. (accessed on 05.02.2024) |
[42] | Green City Energy: Darum können die Anleihen derzeit nicht gehandelt werden (2021) ECOreporter GmbH. Available from: https://www.ecoreporter.de/artikel/green-city-energy-darum-k%C3%B6nnen-die-anleihen-derzeit-nicht-gehandelt-werden/. (accessed on 04.09.2023) |
[43] | Green City Refinanzierungsvehikel forcieren Bond Delisting (2022) BondGuide Media GmbH. Available from: https://www.bondguide.de/topnews/green-city-refinanzierungsvehikel-forcieren-bond-delisting/. (accessed on 03.09.2023) |
[44] | Green City: Verdacht auf zweckentfremdete Gelder (2022) pv magazine group GmbH & Co. KG. Available from: https://www.pv-magazine.de/2022/04/13/green-city-verdacht-auf-zweckentfremdete-gelder/. (accessed on 04.09.2023) |
[45] |
Grumann L, Madaleno M, Vieira E (2024) Gender Differences in Knowledge, Experience, and Preference of Sustainable Investments. J Financ Couns Plan 35: 58–71. https://doi.org/10.1891/JFCP-2022-0050 doi: 10.1891/JFCP-2022-0050
![]() |
[46] |
He F, Yan Y, Hao J, et al. (2022) Retail investor attention and corporate green innovation: Evidence from China. Energ Econ 115: 106308. https://doi.org/https://doi.org/10.1016/j.eneco.2022.106308 doi: 10.1016/j.eneco.2022.106308
![]() |
[47] |
He JM, Iqbal W, Su FL (2023) Nexus between renewable energy investment, green finance, and sustainable development: Role of industrial structure and technical innovations. Renew Energ 210: 715–724. https://doi.org/10.1016/j.renene.2023.04.010 doi: 10.1016/j.renene.2023.04.010
![]() |
[48] | Horn M (2024) The European green deal, retail investors and sustainable investments: A perspective article covering economic, behavioral, and regulatory insights. Available from: https://www.sciencedirect.com/science/article/pii/S266604902400001X?via%3Dihub. (accessed on 21.07.2024) |
[49] |
Hsu PH, Tian X, Xu Y (2014) Financial development and innovation: Cross-country evidence. J Financ Econ 112: 116–135. https://doi.org/10.1016/j.jfineco.2013.12.002 doi: 10.1016/j.jfineco.2013.12.002
![]() |
[50] |
Ibrahim RL, Huang Y, Mohammed A, et al. (2023) Natural resources-sustainable environment conflicts amidst COP26 resolutions: investigating the role of renewable energy, technology innovations, green finance, and structural change. Int J Sust Dev World Ecology 30: 445–457. https://doi.org/10.1080/13504509.2022.2162147 doi: 10.1080/13504509.2022.2162147
![]() |
[51] | Informationen für Anleihegläubiger der Green City-Gruppe (2023) Available from: https://www.dentonsgmbh.com/de/about-dentons-gmbh/information-for-green-city-group-bondholders. (accessed on 13.02.2024) |
[52] | Inhaberschuldverschreibungen Kraftwerkspark Ⅲ Wertpapierprospekt (2016) Green City Energy Kraftwerkspark Ⅲ GmbH & Co. KG. Available from: https://www.vcd-service.de/fileadmin/user_upload/redaktion/beteiligung/2017/pdf/green_city/Verkaufsunterlagen_KWPIII_Wertpapierprospekt_Nachtrag1_03_2017.pdf. (accessed on 03.09.2023) |
[53] |
Islam SM (2023) Impact Risk Management in Impact Investing: How Impact Investing Organizations Adopt Control Mechanisms to Manage Their Impact Risk. J Manag Account Res 35: 115–139. https://doi.org/10.2308/jmar-2021-041 doi: 10.2308/jmar-2021-041
![]() |
[54] | Jahresabschluss per 31.12.2022 (2023)[Financial Reporting]. Available from: https://www.prokon.net/files/Prokon-eG_Jahresabschluss-2022_gez.pdf. (accessed on 29.01.2024) |
[55] | Jahresabschluss zum 31. Dezember 2012. P. R. E. GmbH. Available from: https://www.bundesanzeiger.de/pub/de/suchergebnis?9. (accessed on 29.01.2024) |
[56] | Janzing B (2014) Anleger nach der Prokon-Pleite - In den Wind geschossen. taz Verlags u. Vertriebs GmbH. Available from: from https://taz.de/Anleger-nach-der-Prokon-Pleite/!5050135/. (accessed on 15.08.2023) |
[57] | Jones R, Baker T, Huet K, et al. (2020) Treating ecological deficit with debt: The practical and political concerns with green bonds. Geoforum 114: 49–58. |
[58] | Kirchner C (2013) Umstrittenes Ökounternehmen - So riskant ist Prokon als Geldanlage. DER SPIEGEL GmbH & Co. KG. Available from: https://www.spiegel.de/wirtschaft/service/prokon-anlage-in-oekounternehmen-ist-riskant-a-929892.html. (accessed on 31.08.2023) |
[59] | Kompetenzprofil Green City AG. Bayern Innovativ - Bayerische Gesellschaft für Innovation und Wissenstransfer mbH. Available from: https://www.bayern-innovativ.de/de/kompetenzprofil/green-city-ag. (accessed on 03.09.2023) |
[60] | Konzernobergesellschaft stellt Insolvenzantrag (2022) Available from: https://gc-ag.org/docs/Kraftwerkspark-II-DGAP-Meldung-24.1.2022.pdf. (accessed on 25.02.2024) |
[61] |
Kouwenberg R, Zheng C (2023) A Review of the Global Climate Finance Literature. Sustainability 15: 1255. https://doi.org/10.3390/su15021255 doi: 10.3390/su15021255
![]() |
[62] |
Koval V, Khaustova V, Lippolis S, et al. (2023) Fundamental Shifts in the EU's Electric Power Sector Development: LMDI Decomposition Analysis. Energies 16: 5478. https://doi.org/10.3390/en16145478 doi: 10.3390/en16145478
![]() |
[63] |
La Monaca S, Assereto M, Byrne J (2018) Clean energy investing in public capital markets: Portfolio benefits of yieldcos. Energ Policy 121: 383–393. https://doi.org/10.1016/j.enpol.2018.06.028 doi: 10.1016/j.enpol.2018.06.028
![]() |
[64] |
Ma F, Cao J, Wang Y, et al. (2023) Dissecting climate change risk and financial market instability: Implications for ecological risk management. Risk Anal. https://doi.org/10.1111/risa.14265 doi: 10.1111/risa.14265
![]() |
[65] |
Matviienko H, Pylypenko O, Putintsev A, et al. (2022) European Union policy on financing eco-innovations in the transition to a green economy. Cuest Políticas 40: 28–48. https://doi.org/10.46398/cuestpol.4075.01 doi: 10.46398/cuestpol.4075.01
![]() |
[66] | Meo M, Abd Karim M (2022) The role of green finance in reducing CO2 emissions: An empirical analysis. Borsa Istanb Rev 22: 169–178. |
[67] |
Mzoughi H, Urom C, Guesmi K (2022) Downside and upside risk spillovers between green finance and energy markets. Financ Res Lett 47: 102612. https://doi.org/10.1016/j.frl.2021.102612 doi: 10.1016/j.frl.2021.102612
![]() |
[68] | Naber NL (2014) Prokon setzt Anleger unter Druck. NDR. Available from: https://www.ndr.de/nachrichten/schleswig-holstein/Prokon-setzt-Anleger-unter-Druck, prokon151.html. (accessed on 01.09.2023) |
[69] | Nagel LM, Neller M (2013) Die Windmacher. Axel Springer Deutschland GmbH. Available from: https://www.welt.de/print/wams/wirtschaft/article115294409/Die-Windmacher.html. (accessed on 02.10.2023) |
[70] |
Navid K (2022) How Many Single Rulebooks? The EU's Patchwork Approach to Ensuring Regulatory Consistency in the Area of Investment Management. Eur Bus Organ Law Rev 23: 347–390. https://doi.org/10.1007/s40804-021-00228-w doi: 10.1007/s40804-021-00228-w
![]() |
[71] | New Approaches to SME and Entrepreneurship Financing: Broadening the Range of Instruments (2015) O. S.-G. o. t. OECD. Available from: https://www.oecd.org/cfe/smes/New-Approaches-SME-full-report.pdf. (accessed on 29.01.2024) |
[72] | Oprean C, Bratian V, Lucian Blaga Univ Sibiu FES (2009) THE ROLE OF PORTFOLIO IN RISK REDUCTION THROUGH DIVERSIFICATION[Proceedings Paper]. Industrial Revolutions, from the Globalization and Post-Globalization Perspective, Vol Iv: Banking, Accounting and Financial Systems from the 21st Century Perspective, 476–480. Available from: https://www.webofscience.com/wos/woscc/full-record/WOS: 000287985000073?SID = EUW1ED0EC3lfxnVvOja8Mwsm6XkMk. (accessed on 29.01.2024) |
[73] |
Osei DB, Alagidede IP, Agbodjah S (2023) Impact Investing in Ghana: A Multiple-Case Study. J Social Entrep, 1-21. https://doi.org/10.1080/19420676.2023.2166093 doi: 10.1080/19420676.2023.2166093
![]() |
[74] |
Ozdurak C (2021) Will clean energy investments provide a more sustainable financial ecosystem? Less carbon and more democracy. Renew Sust Energ Rev 151: 111556. https://doi.org/10.1016/j.rser.2021.111556 doi: 10.1016/j.rser.2021.111556
![]() |
[75] |
Pástor, Stambaugh RF, Taylor LA (2021) Sustainable investing in equilibrium. J Financ Econ 142: 550–571. https://doi.org/10.1016/j.jfineco.2020.12.011 doi: 10.1016/j.jfineco.2020.12.011
![]() |
[76] |
Pedersen LH, Fitzgibbons S, Pomorski L (2021) Responsible investing: The ESG-efficient frontier. J Financ Econ 142: 572–597. https://doi.org/https://doi.org/10.1016/j.jfineco.2020.11.001 doi: 10.1016/j.jfineco.2020.11.001
![]() |
[77] |
Polzin F, Sanders M (2020) How to finance the transition to low-carbon energy in Europe? Energ Policy 147: 111863. https://doi.org/10.1016/j.enpol.2020.111863 doi: 10.1016/j.enpol.2020.111863
![]() |
[78] | Prokon droht Anlegern mit Insolvenz (2014) G+J Medien GmbH. Available from: https://www.stern.de/wirtschaft/news/oekostromfinanzierer-prokon-droht-anlegern-mit-insolvenz-3129970.html. (accessed on 30.08.2023) |
[79] | PROKON Genussrechte - Verkaufsprospekt (2010 and 2012) Available from: https://www.anleihen-finder.de/wp-content/uploads/2012/12/Prokon-Wertpapierprospekt-2012-03-21.pdf. (accessed on 06.02.2024) |
[80] | Prokon Insolvenz - Hoffnung für Anleger (2014) Stiftung Warentest. Available from: https://www.test.de/Prokon-Insolvenz-Hoffnung-fuer-Anleger-4701648-0/#: ~: text = Die. (accessed on 30.08.2023) |
[81] |
Purkayastha D, Sarkar R (2021) Getting Financial Markets to Work for Climate Finance. J Struct Financ 27: 27–41. https://doi.org/10.3905/jsf.2021.1.122 doi: 10.3905/jsf.2021.1.122
![]() |
[82] | Qair enters German renewables market (2022) Renews Limited. Available from: https://renews.biz/78268/qair-enters-german-renewables-market/. (accessed on 05.09.2023) |
[83] | Rendite festverzinslicher Wertpapiere bis 2023 (2024) Statista Research Department. Available from: from https://de.statista.com/statistik/daten/studie/192860/umfrage/entwicklung-der-renditen-festverzinslicher-wertpapiere-in-deutschland-seit-2000/. (accessed on 15.02.2024) |
[84] | Resch J (2013a) PROKON: kein vollständiger Konzernabschluss in Sicht. RA Jochen Resch. Available from: https://www.anwalt.de/rechtstipps/prokon-kein-vollstaendiger-konzernabschluss-in-sicht_050643.html. (accessed on 31.08.2023) |
[85] | Resch J (2013b) PROKON: Konzernabschluss lässt noch immer auf sich warten. RA Jochen Resch. Available from: https://www.anwalt.de/rechtstipps/prokon-konzernabschluss-laesst-noch-immer-auf-sich-warten_047997.html. (accessed on 31.08.2023) |
[86] |
Ribas WP, Pedroso B, Vargas LM, et al. (2022) Cooperative Organization and Its Characteristics in Economic and Social Development (1995 to 2020) Sustainability 14: 8470. https://doi.org/10.3390/su14148470 doi: 10.3390/su14148470
![]() |
[87] |
Ringel M, Mjekic S (2023) Analyzing the Role of Banks in Providing Green Finance for Retail Customers: The Case of Germany. Sustainability 15: 8745. https://doi.org/10.3390/su15118745 doi: 10.3390/su15118745
![]() |
[88] | Ruedinger A (2019) Participatory and citizen renewable energy projects in France - State of play and recommendations. Available from: https://inis.iaea.org/search/searchsinglerecord.aspx?recordsFor = SingleRecord & RN = 52116570. (accessed on 08.01.2024) |
[89] |
Santos TMO, Bessani M, Da Silva I (2023) Evolving Dynamic Bayesian Networks for CO(2)Emissions Forecasting in Multi-Source Power Generation Systems. Ieee Latin Am Trans 21: 1022–1031. https://doi.org/10.1109/tla.2023.10251809 doi: 10.1109/tla.2023.10251809
![]() |
[90] | Schlechtere Entwicklung bei Siemens Gamesa drückt auf Siemens Energy (2022) Wirtschaftswoche Dieter von Holtzbrinck. Available from: https://www.wiwo.de/energietechnik-konzern-schlechtere-entwicklung-bei-siemens-gamesa-drueckt-auf-siemens-energy/27995374.html. (accessed on 10.09.2023) |
[91] | Schwere Materialmängel: Windrad in Haltern gesprengt (2023) Landwirtschaftsverlag GmbH. Available from: https://www.topagrar.com/energie/news/schwere-materialmaengel-windrad-in-haltern-gesprengt-13388506.html. (accessed on 05.02.2024) |
[92] |
Selvapandian G, Jeyapaul PP, Gunabalan B (2022) ADOPTION OF GREEN FINANCING STRATEGIES WITH RENEWABLE ENERGY RESOURCES FOR GLOBAL ECONOMIC GROWTH. Glob Econ J 22: 17. https://doi.org/10.1142/s2194565923500045 doi: 10.1142/s2194565923500045
![]() |
[93] | Siemens Gamesa's shareholders approve delisting of the company (2023) Available from: https://www.siemensgamesa.com/-/media/siemensgamesa/downloads/en/newsroom/2023/01/siemens-gamesa-press-release-extraordinary-general-meeting-2023.pdf?ste_sid = d1a81c358371bc49eeabe7877b7a467e. (accessed on 08.01.2024) |
[94] |
Steffen B (2018) The importance of project finance for renewable energy projects. Energ Econ 69: 280–294. https://doi.org/10.1016/j.eneco.2017.11.006 doi: 10.1016/j.eneco.2017.11.006
![]() |
[95] |
Sun X, Zhang A, Zhu M (2023) Impact of Pilot Zones for Green Finance Reform and Innovations on green technology innovations: evidence from Chinese manufacturing corporates. Environ Sci Pollut R 30: 43901–43913. https://doi.org/10.1007/s11356-023-25371-4 doi: 10.1007/s11356-023-25371-4
![]() |
[96] | Sustainability Report 2022 - Tackling challenges (2023) S. E. AG. Available from: https://assets.siemens-energy.com/siemens/assets/api/uuid: 49bc59fb-7af0-47fc-a764-a3550d4153dc/siemens-energy-sustainability-report-2022.pdf. (accessed on 25.02.2024) |
[97] | Sustainable Europe Investment Plan, European Green Deal Investment Plan (COM(2020) 21 final) (2020) European Commission Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri = CELEX: 52020DC0021 & from = EN. (accessed on 29.11.2023) |
[98] |
Taghizadeh-Hesary F, Li YF, Rasoulinezhad E, et al. (2022) Green finance and the economic feasibility of hydrogen projects. Int J Hydrogen Energ 47: 24511–24522. https://doi.org/10.1016/j.ijhydene.2022.01.111 doi: 10.1016/j.ijhydene.2022.01.111
![]() |
[99] |
Taghizadeh-Hesary F, Yoshino N (2019) The way to induce private participation in green finance and investment. Financ Res Lett 31: 98–103. https://doi.org/10.1016/j.frl.2019.04.016 doi: 10.1016/j.frl.2019.04.016
![]() |
[100] |
Taghizadeh-Hesary F, Yoshino N (2020) Sustainable Solutions for Green Financing and Investment in Renewable Energy Projects. Energies 13: 788. https://doi.org/10.3390/en13040788 doi: 10.3390/en13040788
![]() |
[101] | Überblick über die betroffenen Emittentinnen, dem Status zum Insolvenzverfahren und eine mögliche Quotenzahlung (2023) Available from: from https://gc-ag.org/docs/Uebersicht_Insolvenz_Green_City_Gesellschaften_12_2023.pdf. (accessed on 20.01.2024) |
[102] | Unabhängige Analyse: Genussrechte von Prokon im ECOanlagecheck - Teil 1 und 2 (2012) ECOreporter GmbH. Available from: https://www.ecoreporter.de/artikel/ecoanlagecheck-genussrechte-von-prokon-teil-1-des-ausfuehrlichen-ecoanlagechecks-25-05-2012. (accessed on 31.08.2023) |
[103] | Unlautere Bewerbung von Genussrechten (2012) OLG Schleswig-Holstein. Available from: https://www.bbh-blog.de/wp-content/uploads/2012/09/OLG-SH-05.09.2012-6-U-14-11.pdf. (accessed on 29.08.2023) |
[104] | Verfahren gegen Prokon-Gründer eingestellt (2017) Gerald Braunberger, Jürgen Kaube, Carsten Knop, Berthold Kohler. Available from: https://www.faz.net/aktuell/finanzen/finanzmarkt/prokon-gruender-carsten-rodbertus-betrugsverfahren-eingestellt-15187554.html. (accessed on 06.02.2024) |
[105] | Verluste bei der Green City AG (2021) Süddeutsche Zeitung GmbH. Available from: https://www.sueddeutsche.de/muenchen/muenchen-green-city-schieflage-1.5495361. (accessed on 05.02.2024) |
[106] | Voluntary cash tender offer (2022) Siemens Gamesa Renewable Energy, S.A. Available from: https://www.siemensgamesa.com/investors-and-shareholders/cash-tender-offer. (accessed on 02.09.2023) |
[107] |
Wang H, Shen H, Li SW (2023) Does green direct financing work in reducing carbon risk? Econ Model 128: 106495. https://doi.org/10.1016/j.econmod.2023.106495 doi: 10.1016/j.econmod.2023.106495
![]() |
[108] |
Wang R, Usman M, Radulescu M, et al. (2023) Achieving ecological sustainability through technological innovations, financial development, foreign direct investment, and energy consumption in developing European countries. Gondwana Res 119: 138–152. https://doi.org/10.1016/j.gr.2023.02.023 doi: 10.1016/j.gr.2023.02.023
![]() |
[109] |
Wang Z, Teng YP, Xie LB (2023) Innovation for renewable energy and energy related greenhouse gases: Evaluating the role of green finance. Sustaine Energy Techn 57: 103279. https://doi.org/10.1016/j.seta.2023.103279 doi: 10.1016/j.seta.2023.103279
![]() |
[110] |
Wasan P, Kumar A, Luthra S (2024) Green Finance Barriers and Solution Strategies for Emerging Economies: The Case of India. IEEE Transact Eng Manage 71: 414–425. https://doi.org/10.1109/tem.2021.3123185 doi: 10.1109/tem.2021.3123185
![]() |
[111] |
Wu L, Liu D, Lin T (2023) The Impact of Climate Change on Financial Stability. Sustainability 15: 11744. https://doi.org/10.3390/su151511744 doi: 10.3390/su151511744
![]() |
[112] | Wulf AB, Julia (2015) M&A-Deals: ZF Friedrichshafen, Henkel, Prokon. F.A.Z.-Fachverlag. Available from: https://www.finance-magazin.de/deals/ma/ma-deals-zf-friedrichshafen-henkel-prokon-29345/. (accessed on 15.09.2023) |
[113] |
Xiang XJ, Liu CJ, Yang M (2022) Who is financing corporate green innovation? Int Rev Econ Financ 78: 321–337. https://doi.org/10.1016/j.iref.2021.12.011 doi: 10.1016/j.iref.2021.12.011
![]() |
[114] | Ziesemer B (2023) Das große Rätsel von Siemens Energy. Gruner + Jahr Deutschland GmbH. Available from: https://www.capital.de/wirtschaft-politik/das-grosse-raetsel-von-siemens-energy-33856088.html. (accessed on 01.10.2023) |
1. | Xin-You Meng, Yu-Qian Wu, Jie Li, Bifurcation analysis of a Singular Nutrient-plankton-fish model with taxation, protected zone and multiple delays, 2020, 10, 2155-3297, 391, 10.3934/naco.2020010 | |
2. | Zhan‐Ping Ma, Hai‐Feng Huo, Hong Xiang, Spatiotemporal patterns induced by delay and cross‐fractional diffusion in a predator‐prey model describing intraguild predation, 2020, 43, 0170-4214, 5179, 10.1002/mma.6259 | |
3. | Xin-You Meng, Yu-Qian Wu, Dynamical analysis of a fuzzy phytoplankton–zooplankton model with refuge, fishery protection and harvesting, 2020, 63, 1598-5865, 361, 10.1007/s12190-020-01321-y | |
4. | Zhewen Chen, Ruimin Zhang, Jiang Li, Shuwen Zhang, Chunjin Wei, A stochastic nutrient-phytoplankton model with viral infection and Markov switching, 2020, 140, 09600779, 110109, 10.1016/j.chaos.2020.110109 | |
5. | Hai-Feng Huo, Shuang-Lin Jing, Xun-Yang Wang, Hong Xiang, Modeling and analysis of a H1N1 model with relapse and effect of Twitter, 2020, 560, 03784371, 125136, 10.1016/j.physa.2020.125136 | |
6. | Xin-You Meng, Ni-Ni Qin, Hai-Feng Huo, Dynamics of a Food Chain Model with Two Infected Predators, 2021, 31, 0218-1274, 2150019, 10.1142/S021812742150019X | |
7. | Xin-You Meng, Jiao-Guo Wang, Dynamical analysis of a delayed diffusive predator–prey model with schooling behaviour and Allee effect, 2020, 14, 1751-3758, 826, 10.1080/17513758.2020.1850892 | |
8. | Xin-You Meng, Jie Li, Dynamical behavior of a delayed prey-predator-scavenger system with fear effect and linear harvesting, 2021, 14, 1793-5245, 2150024, 10.1142/S1793524521500248 | |
9. | Guangxun Sun, Binxiang Dai, Stability and bifurcation of a delayed diffusive predator-prey system with food-limited and nonlinear harvesting, 2020, 17, 1551-0018, 3520, 10.3934/mbe.2020199 | |
10. | Vikas Kumar, Bulti Pramanick, Impact of nanoparticles on the dynamics of a Crowley–Martin type phytoplankton–zooplankton interaction model, 2022, 8, 26667207, 100139, 10.1016/j.rico.2022.100139 | |
11. | A. Q. Khan, F. Nazir, M. B. Almatrafi, Bifurcation analysis of a discrete Phytoplankton–Zooplankton model with linear predational response function and toxic substance distribution, 2023, 16, 1793-5245, 10.1142/S1793524522500954 | |
12. | Xin-You Meng, Tao Zhang, The impact of media on the spatiotemporal pattern dynamics of a reaction-diffusion epidemic model, 2020, 17, 1551-0018, 4034, 10.3934/mbe.2020223 | |
13. | Bidhan Bhunia, Tapan Kumar Kar, Papiya Debnath, Explicit impacts of harvesting on a delayed predator–prey system with Allee effect, 2023, 2195-268X, 10.1007/s40435-023-01167-9 | |
14. | Poulomi Basak, Satish Kumar Tiwari, Jai Prakash Tripathi, Vandana Tiwari, Ratnesh Kumar Mishra, Plankton interaction model: Effect of prey refuge and harvesting, 2024, 12, 2544-7297, 10.1515/cmb-2024-0011 | |
15. | Shuanghong Zhang, Han Yang, Fanqi Yu, Fuzzy Optimal Guaranteed Cost Control of a Single Specie Bio-Economic Singular Systems in Algae Environment, 2024, 12, 2169-3536, 116218, 10.1109/ACCESS.2024.3445150 | |
16. | Chirodeep Mondal, Dipak Kesh, Debasis Mukherjee, Global stability and bifurcation analysis of an infochemical induced three species discrete-time phytoplankton–zooplankton model, 2023, 176, 09600779, 114136, 10.1016/j.chaos.2023.114136 | |
17. | TIANCAI LIAO, XINYANG RUAN, DETERMINISTIC AND STOCHASTIC ANALYSIS OF A SIZE-DEPENDENT PHYTOPLANKTON–ZOOPLANKTON MODEL WITH ADDITIVE ALLEE EFFECT, 2024, 32, 0218-3390, 271, 10.1142/S0218339024500116 | |
18. | Divakar Vikram Singh, Pankaj Kumar Tiwari, Maitri Verma, A nonautonomous model for the effect of rarity value on the dynamics of a predator–prey system with variable harvesting, 2024, 47, 0170-4214, 5764, 10.1002/mma.9890 | |
19. | Md Golam Mortuja, Mithilesh Kumar Chaube, Santosh Kumar, Dynamic analysis of a modified Leslie-Gower model with Michaelis-Menten prey harvesting and additive allee effect on predator population, 2024, 99, 0031-8949, 115257, 10.1088/1402-4896/ad848a | |
20. | Muhammad Zafarullah Baber, Nauman Ahmed, Muhammad Sajid Iqbal, Muhammad Waqas Yasin, Exact solitary wave solutions and their comparisons under the effect of noise: an allelopathic phytoplankton competition model, 2024, 139, 2190-5444, 10.1140/epjp/s13360-024-05662-6 | |
21. | Qing Guo, Lijun Wang, He Liu, Yi Wang, Jianbing Li, Pankaj Kumar Tiwari, Min Zhao, Chuanjun Dai, Stability switches and chaos induced by delay in a reaction-diffusion nutrient-plankton model, 2023, 17, 1751-3758, 10.1080/17513758.2023.2272852 | |
22. | Rakesh Kumar, Ritu Ahuja, 2024, Chapter 21, 978-981-99-9039-9, 295, 10.1007/978-981-99-9040-5_21 | |
23. | Nazmul Sk, Sayan Mandal, Pankaj Kumar Tiwari, Joydev Chattopadhyay, Exploring multistability and bifurcations in a three-species Smith growth model incorporating refuge, harvesting, and time delays, 2024, 139, 2190-5444, 10.1140/epjp/s13360-024-05874-w |
Parameter | Description | Value |
r | The intrinsic growth rate of phytoplankton | 0.8 |
K | Environmental carrying capacity of phytoplankton | 500 |
β1 | Predation rate of zooplankton | 1 |
β2 | Growth efficiency of zooplankton | 0.89 |
d | Mortality rate of zooplankton | 0.2 |
a1 | Half saturation constant | 0.5 |
a2 | Half saturation constant | 9.2 |
θ | Toxin production rate | 0.39 |
m | Refuge capacity | defaulted |
K0 | Critical level of the growth of phytoplankton | 25 |
q1 | Catchability cofficient of phytoplankton | 0.03 |
q2 | Catchability cofficient of zooplankton | 0.03 |
E | Combined harvesting effort | 10 |
Parameter | Description | Value |
r | The intrinsic growth rate of phytoplankton | 0.8 |
K | Environmental carrying capacity of phytoplankton | 500 |
β1 | Predation rate of zooplankton | 1 |
β2 | Growth efficiency of zooplankton | 0.89 |
d | Mortality rate of zooplankton | 0.2 |
a1 | Half saturation constant | 0.5 |
a2 | Half saturation constant | 9.2 |
θ | Toxin production rate | 0.39 |
m | Refuge capacity | defaulted |
K0 | Critical level of the growth of phytoplankton | 25 |
q1 | Catchability cofficient of phytoplankton | 0.03 |
q2 | Catchability cofficient of zooplankton | 0.03 |
E | Combined harvesting effort | 10 |