Loading [MathJax]/jax/element/mml/optable/MathOperators.js
Research article Special Issues

The impact of climate change on China's central region grain production: evidence from spatiotemporal pattern evolution

  • Received: 31 March 2024 Revised: 27 May 2024 Accepted: 13 June 2024 Published: 24 June 2024
  • Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.

    Citation: Hongtao Wang, Jiajun Xu, Noor Hashimah Hashim Lim, Wanying Liao, Chng Saun Fong. The impact of climate change on China's central region grain production: evidence from spatiotemporal pattern evolution[J]. AIMS Geosciences, 2024, 10(3): 460-483. doi: 10.3934/geosci.2024024

    Related Papers:

    [1] Yong-Ki Ma, N. Valliammal, K. Jothimani, V. Vijayakumar . Solvability and controllability of second-order non-autonomous impulsive neutral evolution hemivariational inequalities. AIMS Mathematics, 2024, 9(10): 26462-26482. doi: 10.3934/math.20241288
    [2] Ahmed Salem, Kholoud N. Alharbi . Fractional infinite time-delay evolution equations with non-instantaneous impulsive. AIMS Mathematics, 2023, 8(6): 12943-12963. doi: 10.3934/math.2023652
    [3] Zainab Alsheekhhussain, Ahmed Gamal Ibrahim, Rabie A. Ramadan . Existence of S-asymptotically ω-periodic solutions for non-instantaneous impulsive semilinear differential equations and inclusions of fractional order 1<α<2. AIMS Mathematics, 2023, 8(1): 76-101. doi: 10.3934/math.2023004
    [4] Misbah Iram Bloach, Muhammad Aslam Noor . Perturbed mixed variational-like inequalities. AIMS Mathematics, 2020, 5(3): 2153-2162. doi: 10.3934/math.2020143
    [5] Lu-Chuan Ceng, Li-Jun Zhu, Tzu-Chien Yin . Modified subgradient extragradient algorithms for systems of generalized equilibria with constraints. AIMS Mathematics, 2023, 8(2): 2961-2994. doi: 10.3934/math.2023154
    [6] Ebrahem A. Algehyne, Abdur Raheem, Mohd Adnan, Asma Afreen, Ahmed Alamer . A study of nonlocal fractional delay differential equations with hemivariational inequality. AIMS Mathematics, 2023, 8(6): 13073-13087. doi: 10.3934/math.2023659
    [7] Qiang Li, Jina Zhao . Extremal solutions for fractional evolution equations of order 1<γ<2. AIMS Mathematics, 2023, 8(11): 25487-25510. doi: 10.3934/math.20231301
    [8] Ramkumar Kasinathan, Ravikumar Kasinathan, Dumitru Baleanu, Anguraj Annamalai . Well posedness of second-order impulsive fractional neutral stochastic differential equations. AIMS Mathematics, 2021, 6(9): 9222-9235. doi: 10.3934/math.2021536
    [9] Chunli You, Linxin Shu, Xiao-bao Shu . Approximate controllability of second-order neutral stochastic differential evolution systems with random impulsive effect and state-dependent delay. AIMS Mathematics, 2024, 9(10): 28906-28930. doi: 10.3934/math.20241403
    [10] Zhi Guang Li . Global regularity and blowup for a class of non-Newtonian polytropic variation-inequality problem from investment-consumption problems. AIMS Mathematics, 2023, 8(8): 18174-18184. doi: 10.3934/math.2023923
  • Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.


    Differential variational inequality is a dynamical system that includes variational inequalities and ordinary differential equations. Differential variational inequalities plays an important role for formulating models involving both dynamics and inequality constraints. Aubin and Cellina [3] introduced the concept of differential variational inequality and after that it was studied by Pang and Stewart [25]. The partial differential variational inequalities was studied by Liu, Zeng and Motreanu [15] and shown that the solution set is compact and continuous. There are some obstacles in their work that constraint set necessarily be compact and only local boundary conditions are satisfied. Liu, Migórskii and Zeng [14] relaxed the conditions of [15] and proved the existence of partial differential variational inequality in non-compact setting. Properties of solution set like strong-weak upper semicontinuity and measurability was proved by them.

    Differential variational inequalities are application oriented and have several applications in engineering and physical sciences, operation research, etc. In particular, they are applicable in electrical circuits with ideal diodes, economical dynamics, dynamic traffic network, functional problems, differential Nash games, control systems, etc., see for example [1,2,16,17,18,19,20,23,26,27,31].

    Evolution equation can be explained as the differential law of the development (evolution) in time of a system. The evolution character of the equation make easier its numerical solution. Variational-like inequality is a generalized form of a variational inequalities and has many applications in operations research, optimization, convex mathematical programming, etc. On the other hand, many problems of engineering and applied sciences can be solved by using second order evolution equation, see for example [5,6,9,10,12,13,22,28,30,32,33].

    Throughout the paper, we assume ~B1 and ~B2 denote separable reflexive Banach spaces and ˆK(ϕ) be convex and closed subset of ~B1. We define some mapping below, that is,

    ˜F:[0,T]×~B2×~B2L(~B1,~B2),˜f:[0,T]×~B2×~B2~B2,˜g:[0,T]×~B2×~B2~B2,˜A:ˆK~B1,η:ˆK׈K~B1,ψ:˜KR{+}, where T>0.

    Inspired by the above discussed work, in this paper, we introduce and study a second order evolutionary partial differential variational-like inequality in Banach spaces. We mention our problem below:

    {y(x)=˜Ay(x)+˜F(x,y(x),y(x))ˆu(x)+˜f(x,y(x),y(x)), a.e. x[0,T],ˆu(x)Sol(˜K,˜g(x,y(x),y(x))+˜A(),ψ), a.e. x[0,T],y(0)=y0,y(0)=y0. (2.1)

    We also consider a variational-like inequality problem of finding ˆu:[0,T]ˆK such that

    ˜g(x,y(x),y(x))+˜A(ˆu(x)),η(ˆv,ˆu(x))+ψ(ˆv)ψ(ˆu(x))0,ˆvˆK, a.e. x[0,T]. (2.2)

    The solution set of problem (2.2) is denoted by Sol[(2.1)].

    The mild solution of problem (2.1) is described by the following definition.

    Definition 2.1. A pair of function (y,ˆu) such that yC1([0,T],~B2) and ˆu:[0,T]ˆK(~B1) measurable, called mild solution of problem (2.1) if

    y(x)=Q(x)y0+R(x)y0+x0R(xp)[˜F(p,y(p),y(p))ˆu(p)+˜f(p,y(p),y(p))]dp,

    where x[0,T] and ˆu(p)Sol(ˆK,˜g(p,y(p),y(p))+A(,ψ). R(x) will be defined in continuation. Here, Sol(ˆK,ˆw+A(.),ψ) denotes the solution set of mixed variational-like inequality (3.1). If (y,ˆu) is a mild solution of above assumed problem, then y is said to be the mild trajectory and ˆu is called the variational control trajectory. Here C1([0,T],~B2) denotes the Banach space of all continuous differentiable mappings y:[0,T]~B2 with norm

    yC1=max{maxx[0,T]y(x),maxx[0,T]y(x)},

    and L(~B2) denotes the Banach space of bounded linear operators from ~B2 into ~B2.

    The subsequent part of this paper is organised in this way. In the next section, some definitions and results are defined, which will be used to achieve our goal. In Section 3, an existence result for variational-like inequalities is proved. Also, we have proved that Sol(ˆK,ˆw+A(.),ψ) is nonempty, closed and convex. The upper semicontinuity of the multi-valued mapping F:[0,T]×~B2×~B2Πbv(^B1) is discussed. In the last section, we have proved that the existence result for the mild solution of second order evolutionary partial differerntial variational-like inequalities under some appropriate conditions.

    Let ^X1 and ^X2 are topological spaces. We shall use Π(^X2) to denote the family of all nonempty subsets of X2, and

    Πc(^X2):={ˆDΠ(^X2):ˆD is closed};

    Πb(^X2):={ˆDΠ(^X2):ˆD is bounded};

    Πbc(^X2):={ˆDΠ(^X2):ˆD is bounded and closed};

    Πcv(^X2):={ˆDΠ(^X2):ˆD is closed and convex};

    Πbv(^X2):={ˆDΠ(^X2):ˆD is bounded and convex};

    Πkv(^X2):={ˆDΠ(^X2):ˆD is compact and convex}.

    One parameter family Q(x), where x is real number, of bounded linear operators from a Banach space ^B2 into itself is called a strongly continuous cosine family if and only if

    (1) Q(x+p)+Q(xp)=2C(x)C(p),x,pR,

    (2) Q(0)=I,(I is the identity operator in ^B2),

    (3) Q(x)w is continuous in x on R for every fixed w^B2.

    We associate with the strongly continuous cosine family Q(x) in ^B2 the strongly continuous sine family R(x), such that

    R(x)W=x0Q(p)wdp,w^B2,xR,

    and the two sets

    E1={w^B2:Q(x)uis one time continuously differentiable in  x  on R},E2={w^B2:Q(x)wis two times continuously differentiable in  x  onR}.

    The operator A:D(A)^B2^B2 is the infinitesimal generator of a strongly continuous cosine family Q(x), xR defined by A(y)=d2/dx2Q(0)ywith D(A)=E2.

    Proposition 2.1. [29] Let Q(x),xR be a strongly continuous cosine family in ^B1. Then the following hold:

    (i) Q(x)=Q(x), xR,

    (ii) Q(p),R(p),Q(x),andR(x)commute x,pR,

    (iii) R(x+p)+Q(xp)=2R(x)Q(p), x,pR,

    (iv) R(x+p)=R(x)Q(p)+R(p)C(x), x,pR,

    (v) R(x)=R(x), xR.

    For furthure information related to the properties of the sine and cosine families, see [12,23,27] and references therein.

    Definition 2.2. [21] Let ^X1,^X2 are topological spaces. Then the multi-valued mapping ˆF:^X1Π(^X2) is said to be:

    (i) Upper semicontinuous (u.s.c., in short) at x^X1, if for each open set U^X2 with ˆF(x)U, a neighbourhood N(x) of x such that

    ˆF(N(x)):=ˆF(y)yN(x)U.

    If ˆF is u.s.c. x^X1, then ˆF is said to be upper semicontinuous on ^X1.

    (ii) Lower semicontinuous (l.s.c., in short) at x^X1 if, for each open set U^X2 satisfying ˆFUϕ, a neighbourhood N(x) of x such that ˆFUϕ yN(x). If ˆF is l.s.c. x^X1, then ˆF is called lower semicontinuous on ^X1.

    Proposition 2.2. [21] Let ˆF:^X1Π(^X2) be a multi-valued mapping, where ^X1,and^X2 denote topological vector spaces. Then the following are equivalent:

    (i) ˆF is upper semicontinuous,

    (ii) the set

    ˆF(C)={x^X1:ˆF(x)Cϕ},

    is closed in ^X1, for each closed set C^X2,

    (iii) the set

    ˆF+(C)={x^X1:ˆF(x)U},

    is open in ^X1, for each open set U^X2.

    Proposition 2.3. [4] Let Ω(ϕ) subset of Banach space ˆX. Assume that the multi-valued mapping ˆF:ΩΠ(ˆX) is weakly compact and convex. Then, ˆF is strongly-weakly u.s.c. if and only if {xn}Ω with xnx0Ω and ynˆF(xn) implies yny0ˆF(x0) up to a subsequence.

    Lemma 2.1. [7] Let {xn} be a sequence such that xnˉx in a normed space V. Then there is a sequence of combinations {yn} such that

    yn=i=nλixi,i=nλi=1andλi0,1i,

    which converges to ˉx in norm.

    Now we define the measurability of a multi-valued mapping, which is needed in the proof of existence of solution of second order evolutionary partial differential variational-like inequality problem (2.1).

    Definition 2.3. [11,21]

    (i) A multi-valued mapping ˆF:IΠ(ˆX) is called measurable if for each open subset UˆX the set ˆF+(U) is measurable in R.

    And

    (ii) the multi-valued mapping ˆF:IΠbc(ˆX) is called strongly measurable if a sequence {ˆFn}n=1 of step set-valued mappings such that

    ˆH(ˆF(t),ˆFn(t))0,asn,tI a.e.,

    here ˆX denotes Banach space, I be an interval of real numbers and ˆH(.,.) denotes the Hausdorff metric on Πbc(ˆX).

    Definition 2.4. [11,34] Let ˆX be Banach space and (F,) be a partial ordered set. A function β:Πb(ˆX)F is called a measure of non compactness (MNC, for short) in ˆX if

    β(¯convO)=β(O)foreveryOΠb(ˆX),

    here ¯convO showing the closure of convex hull of O.

    Definition 2.5. [34] A measure of non compactness β is called

    (i) monotone, if O0,O1Πb(ˆX) and O0O1 implies β(O0)β(O1),

    (ii) nonsingular, if β(aO)=β(O) aˆX and OΠb(ˆX),

    (iii) invariant with respect to union of compact set, if β(KO)=β(O) for each relatively compact set KˆX and OΠb(ˆX),

    (iv) algebraically semiadditive, if β(O0+O1)β(O0)+β(O1) for every O0,O1Πb(ˆX),

    (v) regular, if β(O)=0 is equivalent to the relative compactness of O.

    A very famous example of measure of non compactness is the following Hausdorff measure of non compactness on C([0,T],ˆX) with 0<T< calculated by the following formula:

    χT(O)=12limδ0supxOmax|t1t2|δx(t1)x(t2)ˆX. (2.3)

    Here, χT(O) is said to be the modulus of equicontinuity of OC([0,T],ˆX). Definition (2.4) is applicable on (2.3).

    Definition 2.6. [11] A multi-valued mapping ˆF:ˆKˆXΠ(ˆX) is said to be condensing relative to measure of non compactness β (or β-condensing) if for each OˆK, we have

    β(ˆF(O))

    That is not relatively compact.

    Definition 2.7. [8] A single valued mapping T:\widehat{K}\to \widehat{X}^* is called relaxed \eta - \alpha monotone if \exists a mapping \eta: \widehat{K}\times \widehat{K}\to \widehat{X} and a real-valued mapping \alpha: \widehat{X} \to \mathbb{R} , with \alpha(tz) = t^p\alpha(z) , { \forall } t > 0, and z\in \widehat{X} , such that

    \begin{equation} \langle Tx-Ty, \eta(x, y)\rangle\geq- \alpha(x-y), {\rm{ \forall }}x, y\in \widehat{K}, \end{equation} (2.4)

    where p > 1 is a constant.

    Definition 2.8. [8] A mapping T:\widehat{K}\to \widehat{X}^* is called \eta -coercive with respect to \psi if \exists x_0\in \widehat{K} such that

    \begin{equation} \underset{x\in K, \; \|x\|\to \infty}{lim \; inf}\frac{\langle T(x)-T(y), \eta(x, x_0)\rangle+\psi(x)-\psi(x_0)}{\|\eta(x, x_0)\|}\to +\infty. \end{equation} (2.5)

    Where \eta: \widehat{K}\times \widehat{K}\to \widehat{X} be a mapping and \psi:\widehat{X}\to \mathbb{R}\cup\{+\infty\} is proper convex lower semicontinuous function.

    Theorem 2.1. [11] Let \widehat{X} be a Banach space and \mathcal{M} its closed convex subset, then the fixed point set of \mathcal{\beta} -condensing multi-valued mapping \widehat{F}:\mathcal{M}\to \Pi_{kv}(\mathcal{M}) is nonempty. That is \mathit{\mbox{Fix}}\widehat{F}: = \{x\in \mathcal{M}:x\in \widehat{F}(x)\}\neq \phi. Where \mathcal{\beta} is a nonsingular measure of non compactness defined on subsets of \mathcal{M} .

    Let \widehat{B_1} and \widehat{B_2} are real reflexive Banach spaces and {\widehat{B_1}^*} be the dual of \widehat{B_1} and \widehat{K} be a nonempty closed, convex subset of \widehat{B_1} .

    We consider the following problem of finding \widehat{\mathfrak{u}}\in \widehat{K} such that

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\geq 0, {\rm{ \forall }}\widehat{\mathfrak{v}}\in \widehat{K}, \end{equation} (3.1)

    where \widehat{\mathfrak{w}}\in \widehat{B_1}^* , \mathcal{A}:\widehat{K}\to \widehat{B_1}^* and \eta:\widehat{K}\times \widehat{K}\to \widehat{B_1}. Problem (3.1) is called generalized mixed variational-like inequality. We prove the following lemma.

    Lemma 3.1. Suppose that the following conditions are satisfied:

    (I_1) \mathcal{A}: \widehat{B_1}\to \widehat{B_1}^* is an \eta -hemicontinuous and \eta - \alpha monotone mapping;

    (I_2) \psi: \widehat{B_1}\to \mathbb{R}\cup\{+\infty\} be a proper convex and lower semicontinuous;

    (I_3) the mapping \widehat{\mathfrak{u}}\to\langle \mathcal{A}\widehat{\mathfrak{z}}, \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}})\rangle is convex, lower semicontinuous for fixed \widehat{\mathfrak{v}}, \widehat{\mathfrak{z}}\in \widehat{K} and \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{u}}) = 0, \mathit{\rm{\forall }\; }\widehat{\mathfrak{u}}\in \widehat{K}.

    Then \widehat{\mathfrak{u}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \psi) , if and only if \widehat{\mathfrak{u}} is the solution of following inequality:

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\geq \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}), \mathit{\rm{ \forall }}\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.2)

    Proof. Let \widehat{\mathfrak{u}} is a solution of problem (3.1), then

    \begin{equation*} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\geq 0. \end{equation*}

    Since \mathcal{A} is relaxed \eta - \alpha monotone, we have

    \begin{eqnarray*} && \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\\ & = &\langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\langle \mathcal{A}(\widehat{\mathfrak{v}})-\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\\ &\geq& \langle \mathcal{A}(\widehat{\mathfrak{v}})-\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle\geq\alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}), {\rm{ \forall }}\widehat{\mathfrak{v}}\in \widehat{K}. \end{eqnarray*}

    Hence, \widehat{\mathfrak{u}} is the solution of inequality (3.2).

    Conversely, let \widehat{\mathfrak{u}}\in \widehat{K} be a solution of problem (3.2) and let \widehat{\mathfrak{v}}\in \widehat{K} be any point \psi(\widehat{\mathfrak{v}}) < \infty . We define \widehat{\mathfrak{v}}_s = (1-s)\widehat{\mathfrak{u}}+s\widehat{\mathfrak{v}}, \; \; s\in (0, 1), then due to convexity of \widehat{K} \widehat{\mathfrak{v}}_s\in K . Since \widehat{\mathfrak{v}}_s\in \widehat{K} is the solution of inequality (3.2), it follows from (I_1) (I_3)

    \begin{eqnarray*} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}_s, \widehat{\mathfrak{u}})\rangle +\psi(\widehat{\mathfrak{v}}_s)-\psi(\widehat{\mathfrak{u}})&\geq& \alpha(\widehat{\mathfrak{v}}_s-\widehat{\mathfrak{u}})\\ \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta((1-s)\widehat{\mathfrak{u}}+s\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +\psi((1-s)\widehat{\mathfrak{u}}+s\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})&\geq& \alpha((1-s)\widehat{\mathfrak{u}}\\ &&+s\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}})\\ \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), (1-s)\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{u}})+s\eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +(1-s)\psi(\widehat{\mathfrak{u}})+s\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})&\geq& \alpha(s(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}})). \end{eqnarray*}

    Using (I_3) , we have

    \begin{eqnarray*} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), s\eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +s(\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}))&\geq&s^p\alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}})\\ \langle \widehat{\mathfrak{w}}+\mathcal{A}((1-s)\widehat{\mathfrak{u}}+s\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +(\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}))&\geq&s^{p-1}\alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}), \end{eqnarray*}

    letting s\to 0^{+} , we get

    \begin{equation*} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\geq 0, {\rm{ \forall }}\widehat{\mathfrak{v}}\in K. \end{equation*}

    Theorem 3.1. Suppose that the conditions (I_1) (I_3) are satisfied. Additionally, if the following conditions hold.

    (I_4) \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}})+\eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}) = 0,

    (I_5) \exists \; \; \widehat{\mathfrak{v}}_0\in \widehat{K}\cap D(\psi) such that

    \begin{equation} \underset{\widehat{\mathfrak{u}}\in \widehat{K}, \; \|\widehat{\mathfrak{u}}\|\to \infty}{lim \; inf}\frac{\langle \mathcal{A}(\widehat{\mathfrak{u}})-\mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\rangle+\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}}_0)}{\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|}\xrightarrow{} +\infty. \end{equation} (3.3)

    Then, Sol(K, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) = \{\widehat{\mathfrak{u}}\in \widehat{K}: \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})\geq0, \mathit{\rm{\forall }\; }\widehat{\mathfrak{v}}\in K\}\neq \phi , bounded, closed and convex, for \widehat{\mathfrak{w}}\in \widehat{B_1}^*.

    Proof. Clearly, Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi)\neq \phi , as \widehat{\mathfrak{v}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) , for each \widehat{\mathfrak{v}}\in \widehat{K}.

    Now, we have to show that Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is bounded. Suppose to contrary that Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is not bounded, then there exists a sequence \{\widehat{\mathfrak{u}}_n\}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) such that \|\widehat{\mathfrak{u}}_n\|_{\widehat{B_1}}\to \infty as n\to \infty . We can consider, \forall n\in \mathbb{N} , \|\widehat{\mathfrak{u}}_n\| > n. By \eta -coercive condition (3.3), \exists a constant M > 0 and a mapping \kappa:[0, \infty)\to [0, \infty) with \kappa(k)\to \infty such that for every \|\widehat{\mathfrak{u}}\|_{\widehat{B_1}}\geq M,

    \begin{equation*} \langle \mathcal{A}(\widehat{\mathfrak{u}})-\mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\rangle+\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}}_0)\geq \kappa(\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}})\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}. \end{equation*}

    Thus, if n is sufficiently large as \kappa(n) > (\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|+\|\widehat{\mathfrak{w}}\|),

    \begin{eqnarray*} 0&\leq& \langle \mathcal{A}(\widehat{\mathfrak{u}}_n)+\widehat{\mathfrak{w}}, \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_n)\rangle+\psi(\widehat{\mathfrak{v}}_0)-\psi(\widehat{\mathfrak{u}}_n)\\ & = &\langle \mathcal{A}(\widehat{\mathfrak{u}}_n), \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_n)\rangle+\langle \widehat{\mathfrak{w}}, \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_n)\rangle+\psi(\widehat{\mathfrak{v}}_0)-\psi(\widehat{\mathfrak{u}}_n)\\ & = &-\langle \mathcal{A}(\widehat{\mathfrak{u}}_n)-\mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}}_0)\rangle+\psi(\widehat{\mathfrak{v}}_0)-\psi(\widehat{\mathfrak{u}}_n)+\langle \mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_n)\rangle\\ &&+\langle \widehat{\mathfrak{w}}, \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_n)\rangle\\ &\leq& -\kappa(\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|)\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|.\|\eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}+\|\widehat{\mathfrak{w}}\|.\|\eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}\\ & = &\|\eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}\Big[-\kappa(\|\eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}})+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|+\|\widehat{\mathfrak{w}}\|\Big]\\ & < &0. \end{eqnarray*}

    Which is not possible. Thus, Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is bounded.

    Now it remains to prove that Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is closed.

    Let \{\widehat{\mathfrak{u}}_n\} be a sequence in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) such that \widehat{\mathfrak{u}}_n\to \widehat{\mathfrak{u}}\in \widehat{K}. Then, \forall n\in \mathbb{N}

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{u}}_n), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}_n)\geq 0, \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.4)

    From Lemmas (3.1) and (3.4) same as

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{u}}_n, \widehat{\mathfrak{v}})\rangle +\psi(\widehat{\mathfrak{u}}_n)-\psi(\widehat{\mathfrak{v}})\geq \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n), \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.5)

    By using (I_4) , we have

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}_n)+ \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n)\leq 0, \ {\rm{ \forall }}\ \widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.6)

    Which implies that

    \begin{equation} \underset{n\to 0^+}{lim\; sup}\{\langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}_n)+ \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n)\}\leq 0, {\rm{ \forall }}\; \widehat{\mathfrak{v}}\in \widehat{K}, \end{equation} (3.7)

    as \widehat{\mathfrak{u}}\to\langle \mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle , \psi and \alpha are lower semicontinuous functions. From (3.7), we have

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}})+ \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}})\leq0, \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}, \end{equation} (3.8)

    that is,

    \begin{equation} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}})\rangle +\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}})\geq \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}), \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.9)

    By Lemma 3.1, we get \widehat{\mathfrak{u}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) , that is Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is closed.

    Lastly, we show that Sol(\widehat{K}, \widehat{\mathfrak{w}}+G(.), \; \psi) is convex. For any \widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) and s\in [0, 1], let \widehat{\mathfrak{v}}_s = (1-s)\widehat{\mathfrak{v}}+s\widehat{\mathfrak{u}} . Since \widehat{K} is convex, so that \widehat{\mathfrak{u}}_s\in \widehat{K} . Using (I_3) and letting s\to 0^+ , we obtain

    \begin{align*} \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}_s, \widehat{\mathfrak{v}})\rangle+\psi(\widehat{\mathfrak{v}}_s)&-\psi(\widehat{\mathfrak{v}})\rangle\\ & = \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta((1-s)\widehat{\mathfrak{v}}+s\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}})\rangle +\psi((1-s)\widehat{\mathfrak{v}}+s\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}})\\ &\leq (1-s)\langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{v}})\rangle +s\langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle\\ &+s(\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}}))\\ &\leq s[\langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}})\rangle+(\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}}))]\\ &\leq0, \end{align*}

    that is,

    \langle \widehat{\mathfrak{w}}+\mathcal{A}(\widehat{\mathfrak{v}}_s), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{v}}_s)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{v}}_s)\geq 0.

    Hence, Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is convex.

    Boundedness of \widehat{\mathfrak{w}} implies that Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) is bounded.

    Theorem 3.2. Suppose that all the conditions and mappings are same as considered in Theorem 3.1. Additionally, \forall \widehat{\mathfrak{w}}\in \overline{B}(n, \widehat{B_1}^*), \exists a constant M_n > 0, depending on n , such that

    \begin{equation} \|\widehat{\mathfrak{u}}\|_{\widehat{B_1}}\leq M_n, \; \mathit{\rm{ \forall }\;}\widehat{\mathfrak{u}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi), \end{equation} (3.10)

    where \overline{B}(n, \widehat{B_1}^*) = \{\widehat{\mathfrak{w}}\in \widehat{B_1}^*:\|\widehat{\mathfrak{w}}\|_{\widehat{B_1}^*}\leq n\}.

    Proof. On contrary let us suppose that \exists N_0 > 0 and

    \begin{equation*} \underset{\widehat{\mathfrak{w}}\in\overline{B}(N_0, \widehat{B_1}^*)}{Sup}\Big\{\|\widehat{\mathfrak{u}}\|_{\widehat{B_1}}:\widehat{\mathfrak{u}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi)\Big\} = +\infty. \end{equation*}

    Therefore, \exists \widehat{\mathfrak{w}}_{\hat{k}}\in \overline{B}(N_0, \widehat{B_1}^*) and \widehat{\mathfrak{u}}_{\hat{k}}\in Sol(\widehat{K}, \widehat{\mathfrak{w}}+\mathcal{A}(.), \; \psi) with \|\eta(\widehat{\mathfrak{u}}_{\hat{k}}, \widehat{\mathfrak{v}}_0)\| > \hat{k} (\hat{k} = 1, 2, 3, \cdots). By \eta -coercivity assumption, \exists a constant M > 0 such that \forall \|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|\geq M and a function \kappa:[0, \infty)\to [0, \infty) with \kappa(\hat{k})\to \infty as \hat{k}\to \infty , we have

    \begin{equation*} \langle \mathcal{A}(\widehat{\mathfrak{u}}), \eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\rangle+\psi(\widehat{\mathfrak{u}})-\psi(\widehat{\mathfrak{v}}_0)\geq \kappa(\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|)\|\eta(\widehat{\mathfrak{u}}, \widehat{\mathfrak{v}}_0)\|_{\widehat{B_1}}. \end{equation*}

    Thus, for \hat{k} > M sufficiently large such that \kappa(\hat{k}) > \frac{N_0+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|}{\hat{k}} , one has

    \begin{eqnarray*} 0&\leq& \langle \widehat{\mathfrak{w}}_{\hat{k}}+\mathcal{A}(\widehat{\mathfrak{u}}_{\hat{k}}), \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\rangle+\psi(\widehat{\mathfrak{v}}_0)-\psi(\widehat{\mathfrak{u}}_{\hat{k}})\\ & = & \langle \widehat{\mathfrak{w}}_{\hat{k}}, \eta(\widehat{\mathfrak{v}}_0, u_{\hat{k}})\rangle- \langle \mathcal{A}(\widehat{\mathfrak{u}}_{\hat{k}})-\mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}_{\hat{k}}, \widehat{\mathfrak{v}}_0)\rangle+\langle \mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}_{\hat{k}}, \widehat{\mathfrak{v}}_0)\rangle\\ &&+\psi(\widehat{\mathfrak{v}}_0)-\psi(\widehat{\mathfrak{u}}_{\hat{k}})\\ & = &\langle \widehat{\mathfrak{w}}_{\hat{k}}, \eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\rangle- [\langle \mathcal{A}(\widehat{\mathfrak{u}}_{\hat{k}})-\mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}_{\hat{k}}, \widehat{\mathfrak{v}}_0)\rangle+\psi(\widehat{\mathfrak{u}}_{\hat{k}})-\psi(\widehat{\mathfrak{v}}_0)]\\ &&+\langle \mathcal{A}(\widehat{\mathfrak{v}}_0), \eta(\widehat{\mathfrak{u}}_{\hat{k}}, \widehat{\mathfrak{v}}_0)\rangle\\ & = &\|\widehat{\mathfrak{w}}_{\hat{k}}\|_{\widehat{B_1}^*}\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|-r(\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|)\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|\\ & = &N_0\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|-r(\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|)\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|\|\eta(\widehat{\mathfrak{v}}_0, \widehat{\mathfrak{u}}_{\hat{k}})\|\\ &\leq& (N_0+\|\mathcal{A}(\widehat{\mathfrak{v}}_0)\|)\hat{k}-r(\hat{k}) < 0, \end{eqnarray*}

    which is a contradiction. Hence our supposition is wrong.

    Let \widetilde{\mathfrak{g}}:[0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}\rightarrow \widehat{B_1}^* be the single valued mapping and a multi-valued mapping \mathfrak{F}:[0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}\to \Pi(\widehat{K}) is defined as follows:

    \begin{equation*} \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)): = \Big\{\widehat{\mathfrak{u}}\in \widehat{K}: \widehat{\mathfrak{u}}\in Sol(\widehat{K}, \widetilde{\mathfrak{g}}(x, \mathfrak{y}(x), \mathfrak{y}'(x))+\mathcal{A}(.), \; \psi)\Big\}. \end{equation*}

    It follows from Theorem 3.1 that \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) is nonempty, bounded, closed and convex that is, \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x))\in \Pi_{bcv}(\widehat{B_1}) \forall (x, \mathfrak{y}(x), \mathfrak{y}'(x))\in [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}.

    Theorem 3.3. Suppose that all the conditions and mappings are same as considered in Theorem 3.1 and the mapping \widetilde{g}:[0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}\to \widehat{B_1}^* is bounded and continuous, then the following assertions hold:

    (i) \mathfrak{F} is strongly weakly u.s.c.;

    (ii) x\to \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) is measurable \forall \mathfrak{y}, \mathfrak{y}'\in \widehat{B_2} ;

    (iii) for every bounded subset \Omega^* = \Omega_1\times \Omega_2 of C^1\Big([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}\Big) , \exists a constant M_{\Omega^*} such that

    \begin{equation} \|\mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x))\|: = sup\{\|\widehat{\mathfrak{u}}\|_{\widehat{B_1}}: \widehat{\mathfrak{u}}\in \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x))\}\leq M_{\Omega^*}, \mathit{\rm{ \forall }\;}x\in [0, \mathcal{T}]\; \end{equation} (3.11)

    \mathit{\mbox{and}}\; (\mathfrak{y}, \mathfrak{y}')\in \Omega^*.

    Proof. () Let \mathcal{C}\subset \widehat{B_1} be any weakly closed subset of \widehat{B_1} , suppose that \{(x_n, \mathfrak{y}_n, \mathfrak{y'}_n)\} \subset [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2} such that (x_n, \mathfrak{y}_n, {\mathfrak{y}'}_n)\to (x^*, \mathfrak{y}^{*}, \mathfrak{y}^*{'}) in [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2} with (x_n, \mathfrak{y}_n, \mathfrak{y}_n{'})\in \mathfrak{F}^{-1}(\mathcal{C}): = \{(x, \mathfrak{y}, \mathfrak{y'})\; |\; \mathcal{C}\cap \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}')\neq \phi\}. Therefore, for any n\in\mathbb{N} , there exists \widehat{\mathfrak{u}}_n\in \mathcal{C}\cap \mathfrak{F}(x_n, \mathfrak{y}_n, \mathfrak{y}_n{'}) such that

    \begin{equation} \langle \widetilde{\mathfrak{g}}(x_n, \mathfrak{y}_n, \mathfrak{y}_n{'})+\mathcal{A}(\widehat{\mathfrak{u}}_n), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}_n)\geq 0, {\rm{ \forall }}\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.12)

    By Lemma 3.1, (3.12) is equivalent to

    \begin{equation} \langle \widetilde{\mathfrak{g}}(x_n, \mathfrak{y}_n, \mathfrak{y}_n{'})+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{v}}_n)\geq \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n), \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.13)

    Which implies that,

    \begin{eqnarray} \underset{n\to 0^+}{lim\; sup}\{\langle \widetilde{\mathfrak{g}}(x_n, \mathfrak{y}_n, \mathfrak{y}_n{'})+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}_n)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{v}}_n)\}\geq \underset{n\to 0^+}{lim\; sup}\{ \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n)\}, \\ \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}.\; \; \; \; \; \; \; \end{eqnarray} (3.14)

    Since \widetilde{\mathfrak{g}} is continuous. Therefore, by Theorem 3.3, it implies that \{\widehat{\mathfrak{u}}_n\} is bounded. Hence, by reflexivity of \widehat{B_1}, we can suppose that \widehat{\mathfrak{u}}_n\to \widehat{\mathfrak{u}}^*\in \mathcal{C} in \widehat{B_1}.

    From (3.14), we get

    \begin{equation} \langle \widetilde{\mathfrak{g}}(x^*, \mathfrak{y}^{*}, \mathfrak{y}^*{'})+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}^*)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}^*)\geq \alpha(\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}^*), \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation} (3.15)

    Using Lemma 3.1, we have

    \begin{equation*} \langle \widetilde{\mathfrak{g}}(x^*, \mathfrak{y}^{*}, \mathfrak{y}^*{'})+\mathcal{A}(\widehat{\mathfrak{v}}), \eta(\widehat{\mathfrak{v}}, \widehat{\mathfrak{u}}^*)\rangle +\psi(\widehat{\mathfrak{v}})-\psi(\widehat{\mathfrak{u}}^*)\geq 0, \; \ {\rm{ \forall }}\;\widehat{\mathfrak{v}}\in \widehat{K}. \end{equation*}

    It follows from weakly closeness of \mathcal{C} that

    \begin{equation*} (x^*, \mathfrak{y}^{*}, \mathfrak{y}^*{'})\in \mathfrak{F}^{-1}(\mathcal{C}): = \{(x, \mathfrak{y}, \mathfrak{y}'):\mathcal{C}\cap \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}')\neq \phi\}. \end{equation*}

    Hence, \mathfrak{F} is strongly weakly u.s.c..

    () Define a set

    L_\lambda: = \{x\in [0, \mathcal{T}]; d(v, \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x))) > \lambda\}, \; { \forall }\; (\mathfrak{y}, \mathfrak{y}')\in \widehat{B_2}\times \widehat{B_2}, \; \widehat{\mathfrak{v}}\in \widehat{B_1}.

    Now we will show that L_\lambda is an open set for all \lambda\geq 0. For this let \{x_n\} \subset (L_\lambda)^c = [0, \mathcal{T}]\setminus L_\lambda be a sequence with x_n\to x. Then \forall n\in \mathbb{N} , we have d(v, \mathfrak{F}(x_n, \mathfrak{y}, \mathfrak{y}'))\leq \lambda . As for every (x, \mathfrak{y}, \mathfrak{y}')\in [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}, the multi-valued mapping \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}') is bounded, closed and convex by Theorem 3.1, we get \forall n\in \mathbb{N}, \widehat{\mathfrak{u}}_n\in \mathfrak{F}(x_n, \mathfrak{y}, \mathfrak{y}') such that \|\widehat{\mathfrak{v}}-\widehat{\mathfrak{u}}_n\|\leq \lambda. By Theorem 3.3, \{\widehat{\mathfrak{u}}_n\} is bounded, so we may assume that \widehat{\mathfrak{u}}_n\rightharpoonup \widehat{\mathfrak{u}}\in \widehat{K}. By (i) , \widehat{\mathfrak{u}}\in \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) . Hence, we obtain

    \begin{equation*} d(v, \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}'))\leq \|\widehat{\mathfrak{u}}-\widehat{\mathfrak{v}}\|_{\widehat{B_1}} = \underset{n\to \infty}{\liminf}\|\widehat{\mathfrak{u}}_n-\widehat{\mathfrak{v}}\|_{\widehat{B_1}}\leq \lambda, \end{equation*}

    that is x\in (L_\lambda)^c , thus [0, \mathfrak{F}]\setminus L_\lambda is closed. Hence, L_\lambda is open, consequently L_\lambda is measurable. By [24,Proposition 6.2.4], the mapping x\mapsto \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}') is measurable \forall (\mathfrak{y}, \mathfrak{y}')\in \widehat{B_2}\times \widehat{B_2} .

    (ⅲ) As \widetilde{\mathfrak{g}} is bounded. Therefore

    \widetilde{\mathfrak{g}}_{\Omega^*}: = \{\widetilde{\mathfrak{g}}(x, \mathfrak{y}(x), \mathfrak{y}'(x)):\; x\in [0, \mathcal{T}] \mbox{ and } (\mathfrak{y}, \mathfrak{y}')\in \Omega^*\},

    is also bounded in \widehat{B_1} for every bounded subset \Omega^* of C^1\Big([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}\Big) . Then, by Theorem 3.3, \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) is bounded, \forall\; x\in [0, \mathcal{T}] and (\mathfrak{y}, \mathfrak{y}')\in\Omega^* . Hence, \exists a constant M_{\Omega^*} > 0 such that 3.11 holds.

    Before proving our main result, we mention that by Theorem 3.3, \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) is measurable and \widehat{B_1} is a separable Banach space. Hence, by [21,Theorem 3.17] \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)) possess a measurable selection \xi such that \xi\in L^\infty \Big([0, \mathcal{T}]; \widehat{B_1}\Big)\subset L^2\Big([0, \mathcal{T}], \widehat{B_1}\Big) \forall (\mathfrak{y}, \mathfrak{y}')\in C^1\Big([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}\Big). So

    \begin{equation} P_\mathfrak{F}(\mathfrak{y}, \mathfrak{y}'): = \Big\{\xi\in L^2([0, \mathcal{T}], \widehat{B_1})\; |\; \xi(t)\in \mathfrak{F}(x, \mathfrak{y}(x), \mathfrak{y}'(x)), \; \; a.e., \; x\in [0, \mathcal{T}]\Big\}, \end{equation} (4.1)

    is well defined \forall (\mathfrak{y}, \mathfrak{y}')\in C^1([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}).

    Lemma 4.1. Suppose that (I_1)-(I_4) hold and \widetilde{g}:[0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}\to \widehat{B_1}^* is bounded and continuous. Then, multi-valued mapping P_\mathfrak{F} is strongly upper semicontinuous.

    Proof. Let \{\mathfrak{y}_n, \mathfrak{y}_n'\}\subset C^1([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}) with (\mathfrak{y}_n, \; \mathfrak{y}_n')\to (\mathfrak{y}_0, \mathfrak{y}_0') in C^1([0, \mathcal{T}], \widehat{B_2}\times \widehat{B_2}) and \xi_n\in P_\mathfrak{F}(\mathfrak{y}_n, \mathfrak{y}_n') for n\in \mathbb{N} . Now, we need to prove that \exists a subsequence of \{\xi_n\} , such that \xi_n\to\xi_0\in P_\mathfrak{F}(\mathfrak{y}_0, \mathfrak{y}_0') .

    Indeed, (I_5) confirms that the sequence \{\xi_n\} is bounded in L^2([0, \mathcal{T}], \widehat{B_1}) . Therefore, we can suppose \xi_n\to \xi_0 weakly in L^2([0, T], \widehat{B_1}) . By Lemma 2.1, there is \{\xi\} , a finite combination of the \{\xi_i: i\geq n\} with \bar{\xi_n}\to \xi_0 converges strongly in L^2([0, \mathcal{T}], \widehat{B_1}).

    Since \mathfrak{F} is strongly weakly upper semicontinuous and (\mathfrak{y}_n, \mathfrak{y}_n')\to (\mathfrak{y}_0, \mathfrak{y}_0')\in C^1([0, \mathcal{T}], \widehat{B_2}), therefore for every weak neighborhood \mathcal{Y}_x of \mathfrak{F}(x, \mathfrak{y}_0(x), \mathfrak{y}_0'(x)) there exists a strong neighborhood

    \mathfrak{F}(x, \mathfrak{y}, \mathfrak{y}')\subset \mathcal{Y}_x, \; \; \; { \forall }\; (\mathfrak{y}, \mathfrak{y}')\in \mathcal{U}_x.

    Which shows that \xi \in P_\mathfrak{F}(\mathfrak{y}_0, \mathfrak{y}_0'). Thus, by Proposition 2.3, P_\mathfrak{F} is strongly upper semi continuous.

    We also need the following assumptions for achieving the goal.

    (I_6) \widetilde{\mathfrak{g}}: [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}\to \widehat{B_1}^* is continuous and bounded;

    (I_7) \widetilde{\mathcal{F}}(., \mathfrak{y}, .):[0, \mathcal{T}]\to \mathcal{L}(\widehat{B_1}, \widehat{B_2}) , \widetilde{\mathcal{F}}(., ., \mathfrak{y}'):[0, \mathcal{T}]\to \mathcal{L}(\widehat{B_1}, \widehat{B_2}) are measurable for all \mathfrak{y}, \mathfrak{y}'\in \widehat{B_2} and \widetilde{\mathcal{F}}(x, ., .):\widehat{B_2}\to \mathcal{L}(\widehat{B_2}, \widehat{B_1}) is continuous for a.e. x\in [0, \mathcal{T}], where \mathcal{L}(\widehat{B_1}, \widehat{B_2}) denotes the class of bounded linear operators from \widehat{B_1} to \widehat{B_2} , and there exists \rho_{\widetilde{\mathcal{F}}}\in L^2([0, \mathcal{T}], \mathbb{R_+}) and a non-decreasing continuous mapping \gamma_{\widetilde{\mathcal{F}}}:[0, \infty)\to [0, \infty) such that

    \|\widetilde{\mathcal{F}}(x, \mathfrak{y}(x), \mathfrak{y}'(x))\|\leq \rho_{\widetilde{\mathcal{F}}}(x)\gamma_{\widetilde{\mathcal{F}}}\Big(\|\mathfrak{y}(x)\|_{\widehat{B_2}}+\|\mathfrak{y}'(x)\|_{\widehat{B_2}}\Big), \ {\rm{ \forall }}\;(x, \mathfrak{y}, \mathfrak{y}')\in [0, \mathcal{T}]\times \widehat{B_2}\times \widehat{B_2}.

    (I_8) \widetilde{\mathfrak{f}}(., \mathfrak{y}, .), \; \widetilde{\mathfrak{f}}(., ., \mathfrak{y}'):[0, \mathcal{T}]\to \widehat{B_2} are measurable for all \mathfrak{y}, \mathfrak{y}'\in \widehat{B_2} and there exists \rho_{\widetilde{\mathfrak{f}}}\in L^2\Big([0, \mathcal{T}], \mathbb{R_+}\Big) such that for x\in [0, \mathcal{T}] \widetilde{\mathfrak{f}}(x, ., .):\widehat{B_2}\to \widehat{B_2} satisfies

    \begin{equation} \begin{cases} \|\widetilde{\mathfrak{f}}(x, \mathfrak{y}, \mathcal{y})-\widetilde{\mathfrak{f}}(x, \mathfrak{y}', \mathcal{y})\|\leq \rho_{\widetilde{\mathfrak{f}}}(x)\|\mathfrak{y}-\mathfrak{y}'\|_{\widehat{B_2}}, \cr \|\widetilde{\mathfrak{f}}(x, \mathfrak{y}, \mathcal{y})-\widetilde{\mathfrak{f}}(x, \mathfrak{y}, \mathcal{y}')\|\leq \rho_{\widetilde{\mathfrak{f}}}(x)\|\mathcal{y}-\mathcal{y}'\|_{\widehat{B_2}}, \cr \|\widetilde{\mathfrak{f}}(x, 0, 0)\|\leq \rho_{\widetilde{\mathfrak{f}}}(x). \end{cases} \end{equation} (4.2)

    The following result ensures the existence of solution of problem (2.1).

    Theorem 4.1. Under the assumptions (I_1) (I_8) , if the following inequalities hold

    \begin{equation} \underset{\hat{k}\to \infty}{lim\; inf}\Big[\frac{\gamma_{\widetilde{\mathcal{F}}}(\hat{k})}{\hat{k}}\|\rho_{\widetilde{\mathcal{F}}}(x)\|M_{\|\widetilde{\mathfrak{g}}\|}+\|\rho_{\widetilde{\mathfrak{f}}}(x)\|_{L^2}+\frac{\|\mathfrak{y}_0\|+\|y_0\|}{\hat{k}\mathcal{T}^{1/2}}\Big] < \frac{1}{\delta \mathcal{T}^{1/2}}, \end{equation} (4.3)
    \begin{equation} \|Q(x_1)-Q(x_2)\|\leq \|x_1-x_2\|\; \mathit{\mbox{and}}\; \|R(x_1)-R(x_2)\|\leq \|x_1-x_2\|, \end{equation} (4.4)

    where

    \begin{equation*} \delta = max\Big\{\underset{x\in J}{sup}\|Q(x)\|_{L(\widehat{B_2})}, \; \; \underset{x\in J}{sup}\|R(x)\|_{L(\widehat{B_2})}\Big\}, \end{equation*}

    and M_{\|\widetilde{\mathfrak{g}}\|} > 0 is a constant stated in Theorem 3.2, then, the problem (2.1) has at least one mild solution (\mathfrak{y}, \widehat{\mathfrak{u}}).

    Proof. We define the multi-valued mapping \Gamma: C^1([0, \mathcal{T}], \widehat{B_2})\to \Pi(C^1([0, \mathcal{T}], \widehat{B_2})) such that

    \begin{eqnarray} \Gamma(\mathfrak{y}): = \Big\{y\in C^1([0, \mathcal{T}], \widehat{B_2})\Big{|}\; y(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)\Big[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\\ +\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\Big]dp, \; x\in [0, \mathcal{T}], \; \xi\in P_\mathfrak{F}(x)\Big\}, \\ \end{eqnarray} (4.5)

    where P_\mathfrak{F} is defined in (4.1). Our aim is to show that Fix(\Gamma)\neq \phi .

    Step-Ⅰ. \Gamma(\mathfrak{y})\in \Pi_{cv}\Big(C^1([0, \mathcal{T}], \widehat{B_2})\Big) for each \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}).

    Clearly, \Gamma(\mathfrak{y}) is convex for every \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}) due to the convexity of P_\mathfrak{F}(\mathfrak{y}).

    Since for each y\in \Gamma(\mathfrak{y}), we can choose \xi\in P_\mathfrak{F}(\mathfrak{y}) such that

    \begin{eqnarray*} y(x)& = &Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp, \end{eqnarray*}

    which implies that,

    \begin{eqnarray*} \|y(x)\|&\leq& \|\mathfrak{y}_0Q(x)\|+\|y_0R(x)\|+\Big\|\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\Big\|\\ &\leq& \delta \|\mathfrak{y}_0\|+\delta \|y_0\|+\delta \Big[\int_{0}^{x}\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\xi(p)\|dp\\ &&+\int_{0}^{x}\|\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|dp\Big]. \end{eqnarray*}

    Using (I_7) and (I_8) and applying Hölder's inequality,

    \begin{eqnarray*} \|y(x)\|&\leq& \delta \|\mathfrak{y}_0\|+\delta \|y_0\|+\delta \Big[\int_{0}^{x}\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\xi(p)\|dp\\ &&+\int_{0}^{x}\|\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|dt\Big], \\ & = &\delta(\|\mathfrak{y}_0\|+\|y_0\|)+\delta \Big[\int_{0}^{x}\rho_{\widetilde{\mathcal{F}}}(p)\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}\|+\|\mathfrak{y}'\|)M_{\|\widetilde{\mathfrak{g}}\|}dp\\ &&+\int_{0}^{x}\rho_{\widetilde{\mathfrak{f}}}(p)(1+\|\mathfrak{y}\|+\|\mathfrak{y}'\|)dp\Big]\\ & = &\delta(\|\mathfrak{y}_0\|+\|y_0\|)+\delta \gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}\|+\|\mathfrak{y}'\|)M_{\|\widetilde{\mathfrak{g}}\|}\int_{0}^{x}\rho_{\widetilde{\mathcal{F}}}(p)dp\\ &&+\delta(1+\|\mathfrak{y}\|+\|\mathfrak{y}'\|)\int_{0}^{x}\rho_{\widetilde{\mathfrak{f}}}(p)dp, \\ & = &\delta\Big(\|\mathfrak{y}_0\|+\|y_0\|+\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}\|+\|\mathfrak{y}'\|)M_{\|\widetilde{\mathfrak{g}}\|}\|\rho_{\widetilde{\mathcal{F}}}\|\mathcal{T}^{1/2}\\ &&+(1+\|\mathfrak{y}\|+\|\mathfrak{y}'\|)\|\rho_{\widetilde{\mathfrak{f}}}\|\mathcal{T}^{1/2}\Big)\\ & = &\delta \mathcal{T}^{1/2}\Big[\frac{\|\mathfrak{y}_0\|+\|y_0\|}{\mathcal{T}^{1/2}}+\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}\|+\|\mathfrak{y}'\|)M_{\|\widetilde{\mathfrak{g}}\|}\|\rho_{\widetilde{\mathcal{F}}}\|\\ &&+(1+\|\mathfrak{y}\|+\|\mathfrak{y}'\|)\|\rho_{\widetilde{\mathfrak{f}}}\|\Big]. \end{eqnarray*}

    Hence, \Gamma(\mathfrak{y}) is bounded in C^1([0, \mathcal{T}], \widehat{B_2}) for each \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}).

    Next we shall prove that \Gamma(\mathfrak{y}) is a collection of equicontinuous mappings \forall\; \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}).

    \begin{eqnarray} \|y(x_2)-y(x_1)\|_{\widehat{B_2}}& = &\Big\|\mathfrak{y}_0Q(x_2)+y_0R(x_2)+\int_{0}^{x_2}R(x_2-p)\Big[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\nonumber\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\Big]dp-\mathfrak{y}_0Q(x_1)-y_0R(x_1)\nonumber\\ &&-\int_{0}^{x_1}R(x_1-t)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\Big\|_{\widehat{B_2}} \\ &\leq&\|\mathfrak{y}_0\|\|Q(x_2)-Q(x_1)\|+\|y_0\|\|R(x_2)-R(x_1)\|\\ &&+\Big\|\int_{0}^{x_2}R(x_2-t)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\\ &&-\int_{0}^{x_1}R(x_1-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\\ &&+\int_{0}^{x_1}R(x_2-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\\ &&-\int_{0}^{x_1}R(x_2-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\Big\|\\ &\leq& \|\mathfrak{y}_0\|\|x_2-x_1\|+\|y_0\|\|x_2-x_1\|+\int_{x_1}^{x_2}\Big\|R(x_2-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \\ &&\mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\Big\|+\int_{0}^{x_1}\Big\|(R(x_2-p) \\ &&-R(x_1-p))[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]\Big\|dp\\ & = &(\|\mathfrak{y}_0\|+\|y_0\|)\|x_2-x_1\|+I_1+I_2, \end{eqnarray} (4.6)
    \begin{eqnarray} \label{eq4.6} \text{where} \quad I_1& = &\int_{x_1}^{x_2}\Big\|R(x_2-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp\Big\|, \\ \text{and}\ \ \ \ \quad I_2& = &\int_{0}^{x_1}\Big\|(R(x_2-p)-R(x_1-p))[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]\Big\|dp. \end{eqnarray}

    Applying Hölder's inequality, we have

    \begin{eqnarray} I_1&\leq& \int_{x_1}^{x_2}\|R(x_2-p)\|\|[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\|dp\\ &&+\int_{x_1}^{x_2}\|R(x_2-p)\|\|\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]\|dp\\ &\leq& \int_{x_1}^{x_2}\delta M_{\|\widetilde{\mathfrak{g}}\|}\rho_{\widetilde{\mathcal{F}}}(p)\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)dp\\ &&+\int_{x_1}^{x_2}\delta\gamma_{\widetilde{\mathfrak{g}}}(1+\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)dp, \\ & = &\delta M_{\|\widetilde{\mathfrak{g}}\|}\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)\|\rho_{\widetilde{\mathcal{F}}}(p)\|(x_2-x_1)^{1/2}\\ &&+\delta \gamma_{\widetilde{\mathfrak{f}}}(1+\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)(x_2-x_1)^{1/2}\\ & = &\delta (x_2-x_1)^{1/2}\Big[M_{\|\widetilde{\mathfrak{g}}\|}\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p))\|\rho_{\widetilde{\mathcal{F}}}(p)\|\\ &&+\gamma_{\widetilde{\mathfrak{f}}}(1+\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)\Big]\rightarrow 0\text{ as } x_1\to x_2. \end{eqnarray} (4.7)

    Further by Proposition 2.2 and (4.4) and Hölder's inequality, we have

    \begin{eqnarray} I_2& = &\int_{0}^{x_1}\Big\|(R(x_2-p)-R(x_1-p))[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]\Big\|dp\\ & = &\Big\|\int_{0}^{x_1}[R(p)(Q(x_2)-Q(x_1))+Q(p)(R(x_1)-R(x_2))]\\ &&\times[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]\Big\|dp\\ &\leq& \int_{0}^{x_1}\|R(p)\|\|Q(x_1)-Q(x_2)\|\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\xi(p)\\ &&+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|dp+\int_{0}^{x_1}\|Q(p)\|\|R(x_1)-R(x_2)\|\\ &&\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\xi(p)+\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|dp\\ &\leq& \int_{0}^{x_1}\delta\|x_1-x_2\|\Big[\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|\|\xi(p)\|\\ &&+\|\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|\Big]dp+\int_{0}^{x_1}\delta \|x_1-x_2\|\\ &&\times[\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|+\|\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}'(p))\|]dp\\ &\leq&2\delta(\|x_1-x_2\|)\int_{0}^{x_1}[M_{\|\widetilde{\mathfrak{g}}\|}\rho_{\widetilde{\mathcal{F}}}(p)\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)\\ &&+\rho_{\widetilde{\mathfrak{f}}}(p)\gamma_{\widetilde{\mathfrak{f}}}(1+\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)]dp, \\ &\leq&2\delta\|x_1-x_2\|\Big[M_{\|\widetilde{\mathfrak{g}}\|}\|\rho_{\widetilde{\mathcal{F}}}(p)\|\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)\\ &&+\gamma_{\widetilde{\mathcal{F}}}(1+\|\mathfrak{y}(p)\|+\|\mathfrak{y}'(p)\|)\Big]x^{1/2}\to0\text{ as } x_1\to x_2. \end{eqnarray} (4.8)

    From (4.6)–(4.8), we have

    \begin{equation*} \|y(x_2)-y(x_1)\|_{\widehat{B_2}}\longrightarrow 0, \; \mbox{as}\; \; x_1\to x_2. \end{equation*}

    Hence, \Gamma(\mathfrak{y}) is equicontinuous, \forall\; \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}). By Arzela-Ascoli theorem [34], we obtained that \Gamma(\mathfrak{y}) is relatively compact \forall\; \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}).

    Now, we have to check that \Gamma(\mathfrak{y}) is closed in C^1([0, \mathcal{T}], \widehat{B_2}) \forall\; \mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}).

    Let \{y_n\}\subset \Gamma(\mathfrak{y}) is a sequence with y_n\to y^* in C^1([0, \mathcal{T}]; \widehat{B_2}) as n\to \infty . Hence, there exist a sequence \{\xi_n\}\subset P_\mathfrak{F}(\mathfrak{y}) such that

    \begin{equation*} y_n(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi_n(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp, \end{equation*}

    x\in [0, \mathcal{T}]. By (iii) of Theorem 3.3, it follows that the sequence \{\xi_n\} is weakly relatively compact. Since P_\mathfrak{F}(\mathfrak{y}) is upper semicontinuity (see Lemma 4.1), we may assume \xi_n\to \xi^*\in P_\mathfrak{F}(\mathfrak{y}) in L^2([0, \mathcal{T}], \widehat{B_1}) , where \xi^*\in P_\mathfrak{F}(\mathfrak{y}) . On the other hand, by strongly continuity of Q(x) and R(x) for x > 0 , we have

    \begin{equation*} y^*(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))\xi^*(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}(p), \mathfrak{y}{'}(p))]dp, \end{equation*}

    x\in [0, \mathcal{T}]. Which implies that y^*\in \Gamma(\mathfrak{y}) , that is \Gamma(\mathfrak{y})\in \Pi_{cv}(C^1[0, \mathcal{T}], \widehat{B_2}) .

    Step-Ⅱ. The multi-valued mapping \Gamma is closed.

    For this assume \mathfrak{y}_n\to \mathfrak{y}^* and y_n\to y^* in C^1([0, \mathcal{T}], \widehat{B_2}) with y_n\in \Gamma(\mathfrak{y}_n) \forall \; n\in \mathbb{N}. We need to prove that y^*\in \Gamma(\mathfrak{y}^*). From the definition of multi-valued map \Gamma , we may take \xi_n\in P_\mathfrak{F}(\mathfrak{y}_n) \forall\; n\in \mathbb{N} such that

    \begin{eqnarray} y_n(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}_n(p), \mathfrak{y}_n{'}(p))\xi_n(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}_n(p), \mathfrak{y}_n{'}(p))]dp, \\ x\in [0, \mathcal{T}].\\ \end{eqnarray} (4.9)

    With the help of Theorem 3.3 and Lemma 4.1, we may consider that \xi_n\rightharpoonup \xi^*\in P_\mathfrak{F}(\mathfrak{y}^*) . By using, I_8 we get that \widetilde{\mathfrak{f}}(., \mathfrak{y}_n(.), \mathfrak{y}_n'(.))\to \widetilde{\mathfrak{f}}(., \mathfrak{y}^*, \mathfrak{y}^*{'}) in L^2([0, \mathcal{T}], \widehat{B_2}) .

    By using the continuity of \widetilde{\mathcal{F}}(x, ., .) and strongly continuity of Q(x) , R(x) for x > 0 , we obtain from (4.9) that

    \begin{eqnarray*} y^*(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}^*(p), \mathfrak{y}^*{'}(p))\xi^*(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}^*(p), \mathfrak{y}^*{'}(p))]dp, \\ x\in [0, \mathcal{T}], \end{eqnarray*}

    and \xi^*\in P_\mathfrak{F}(\mathfrak{y}) . Thus \mathfrak{y}^*\in \Gamma(\mathfrak{y}^*).

    Step-Ⅲ. \Gamma is \chi_T condensing.

    Let \mathcal{D}\subset \Pi_b(C^1([0, \mathcal{T}], \widehat{B_2})). Therefore, \mathcal{D} is not relatively compact subset of C^1([0, \mathcal{T}], \widehat{B_2}). For \mathcal{D} , we need to prove that \chi_\mathcal{T}(\mathcal{(D)})\nleq \chi_\mathcal{T}(\Gamma(\mathcal{(D)})). Since \mathcal{D} is bounded subset of C^1([0, \mathcal{T}], \widehat{B_2}) , then by applying the same technique as in Step-I, we may prove that \Gamma(\mathcal{(D)}) is relatively compact, that is, \chi_\mathcal{T}(\mathcal{D}) = 0. Hence, \chi_\mathcal{T}(\mathcal{(D)})\leq \chi_\mathcal{T}(\Gamma(\mathcal{D})) = 0 implies that \mathcal{D} is relatively compact by regularity of \chi_T , we conclude that \Gamma is \chi_\mathcal{T} -condensing.

    Step-Ⅳ. \exists a constant M_\mathfrak{R} > 0 such that

    \begin{equation} \Gamma(\bar{B}_{M_\mathfrak{R}}\subset \bar{B}{M_\mathfrak{R}}): = \{\mathfrak{y}\in C^1([0, \mathcal{T}], \widehat{B_2}):\|\mathfrak{y}\|_C\leq M_\mathfrak{R}\}\subset C^1([0, \mathcal{T}], \widehat{B_2}). \end{equation} (4.10)

    Let us assume that \forall k > 0, \exists two sequences \{\mathfrak{y}_k\} and \{y_k\} such that

    \|\mathfrak{y}_k\|_{C^1([0, \mathcal{T}], \widehat{B_2})}, \; \|\mathfrak{y}_k^{'}\|_{C^1([0, \mathcal{T}], \widehat{B_2})}\leq k/2 and y_k\in \Gamma(\mathfrak{y}_k) such that \|y_k\| > 0 . Hence, there is \xi_k\in P_\mathfrak{F}(\mathfrak{y}_k) such that

    \begin{eqnarray*} y_k(x) = Q(x)\mathfrak{y}_0+R(x)y_0+\int_{0}^{x}R(x-p)[\widetilde{\mathcal{F}}(p, \mathfrak{y}_k(p), \mathfrak{y}_k{'}(p))\xi_k(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}_k(p), \mathfrak{y}_k{'}(p))]dp, \\ \; \; x\in [0, \mathcal{T}]. \end{eqnarray*}

    Using Hölder's inequality, for every x\in [0, \mathcal{T}] , we have

    \begin{eqnarray*} \|\mathfrak{y}_k(x)\|&\leq& \|Q(x)\|\|\mathfrak{y}_0\|+\|R(x)\|\|y_0\|\\ && +\int_{0}^{x}\|R(x-p)\|\|[\widetilde{\mathcal{F}}(p, \mathfrak{y}_k(p), \mathfrak{y}_k{'}(p))\xi_k(p) +\widetilde{\mathfrak{f}}(p, \mathfrak{y}_k(p), \mathfrak{y}_k{'}(p))]\|dp, \\ & = &\delta(\|\mathfrak{y}_0\|+\|y_0\|)+\int_{0}^{x}\delta[\gamma_{\widetilde{\mathcal{F}}}(\|\mathfrak{y}_k\|+\|\mathfrak{y}_k{'}\|)\rho_{\widetilde{\mathcal{F}}}(p)M_{\|\widetilde{\mathfrak{f}}\|}]dp\\ &&+\int_{0}^{x}\delta\gamma_{\widetilde{\mathfrak{f}}}(1+\|\mathfrak{y}_k\|+\|\mathfrak{y}_k^{'}\|)\rho_{\widetilde{\mathfrak{f}}}(p)dp\\ &\leq&\delta(\|\mathfrak{y}_0\|+\|y_0\|)+\int_{0}^{x}\delta[\gamma_{\widetilde{\mathcal{F}}}(k)\rho_{\widetilde{\mathcal{F}}}(p)M_{\|\widetilde{\mathfrak{g}}\|}]dp\\ &&+\int_{0}^{x}\delta\gamma_{\widetilde{\mathfrak{f}}}(1+k)\rho_{\widetilde{\mathfrak{f}}}(p)dp\\ &\leq&\delta(\|\mathfrak{y}_0\|+\|y_0\|)+\delta\gamma_{\widetilde{\mathcal{F}}}(k)\|\rho_{\widetilde{\mathcal{F}}}(x)\|M_{\|\widetilde{\mathfrak{g}}\|}\mathcal{T}^{1/2}\\ &&+\delta\gamma_{\widetilde{\mathfrak{f}}}(1+\kappa)\|\rho_{\widetilde{\mathfrak{f}}}(x)\|\mathcal{T}^{1/2}\\ & = &\delta \mathcal{T}^{1/2}\Big[\gamma_{\widetilde{\mathcal{F}}}(k)\|\rho_{\widetilde{\mathcal{F}}}(x)\|M_{\|\widetilde{\mathfrak{g}}\|}+\gamma_{\widetilde{\mathfrak{f}}}(1+k)\|\rho_{\widetilde{\mathfrak{g}}}(x)\|+\frac{\|\mathfrak{y}_0\|+\|y_0\|}{\mathcal{T}^{1/2}}\Big], \end{eqnarray*}

    we obtain by using (4.9),

    \begin{eqnarray*} 1&\leq& \underset{k\to \infty}{lim\; inf}\frac{\|y_k\|_{C^1([0, \mathcal{T}], \widehat{B_2})}}{k}\\ &\leq&\underset{k\to \infty}{lim\; inf}\Big[\gamma_{\widetilde{\mathcal{F}}}(k)\|\rho_{\widetilde{\mathcal{F}}}(x)\|M_{\|\widetilde{\mathfrak{g}}\|}\mathcal{T}^{1/2}+\gamma_{\widetilde{\mathfrak{f}}}(1+k)\|\rho_{\widetilde{\mathfrak{f}}}(x)\|\mathcal{T}^{1/2}+(\|\mathfrak{y}_0\|+\|y_0\|)\Big]\\ & < &1, \end{eqnarray*}

    which is a contradiction. Therefore there exists M_\mathfrak{R} such that (4.10) holds.

    Thus, all requirements of Theorem 2.1 are fulfilled. This implies that Fix\Gamma\neq \phi in \overline{B}_{M_\mathfrak{R}}. Therefore, (SOEPDVLI) has at least one mild solution (\mathfrak{y}, \widehat{\mathfrak{u}}).

    In this paper, a second order evolutionary partial differential variational-like inequality problem is introduced and studied in a Banach space, which is much more general than the considered by Liu-Migórski-Zeng [14], Li-Huang-O'Regan [13] and Wang-Li-Li-Huang [33] etc. We investigate suitable conditions to prove an existence theorem for our problem by using the theory of strongly continuous cosine family of bounded linear operator, fixed point theorem for condensing set-valued mapping and the theory of measure of non-compactness.

    The authors are highly thankful to anonymous referees and the editor for their valuable suggestions and comments which improve the manuscript a lot.

    The authors declare that they have no conflicts of interest.



    [1] Hou M, Deng Y, Yao S (2021) Spatial Agglomeration Pattern and Driving Factors of Grain Production in China since the Reform and Opening Up. Land 10: 10. https://doi.org/10.3390/land10010010 doi: 10.3390/land10010010
    [2] Lan Y, Xu B, Huan Y, et al. (2023) Food Security and Land Use under Sustainable Development Goals: Insights from Food Supply to Demand Side and Limited Arable Land in China. Foods 12: 4168. https://doi.org/10.3390/foods12224168 doi: 10.3390/foods12224168
    [3] Food and Agriculture Organization of the United Nations (2021) The State of Food Security and Nutrition in the World 2021: The world is at a critical juncture. Available from: https://www.fao.org/state-of-food-security-nutrition/2021/en/.
    [4] Qu H, Li J, Wang W, et al. (2022) New Insight into the Coupled Grain-Disaster-Economy System Based on a Multilayer Network: An Empirical Study in China. ISPRS Int J Geo-Inf 11: 59. https://doi.org/10.3390/ijgi11010059 doi: 10.3390/ijgi11010059
    [5] Chen L, Chen X, Pan W, et al. (2023) Assessing Rural Production Space Quality and Influencing Factors in Typical Grain-Producing Areas of Northeastern China. Sustainability 15: 14286. https://doi.org/10.3390/su151914286 doi: 10.3390/su151914286
    [6] Liu L, Ruan R (2016) A review of the impact of climate warming on grain security. Jiangsu Agric Sci 11: 6–10. https://doi.org/10.15889/j.issn.1002-1302.2016.11.002 doi: 10.15889/j.issn.1002-1302.2016.11.002
    [7] Kogo BK, Kumar L, Koech R (2021) Climate change and variability in Kenya: a review of impacts on agriculture and food security. Environ Dev Sustain 23: 23–43. https://doi.org/10.1007/s10668-020-00589-1 doi: 10.1007/s10668-020-00589-1
    [8] Dahe Net - Henan Daily (2010) Major Measures to Create a New Situation for the Rise of the Central Region - Planning Interpretation. Henan Province Bureau of Statistics. Available from: https://tjj.henan.gov.cn/2010/01-04/1364162.html.
    [9] Liu H (2023) Division of Grain Production Zones to be Improved. Ministry of Agriculture and Rural Affairs of the People's Republic of China. Available from: http://www.moa.gov.cn/ztzl/ymksn/jjrbbd/202308/t20230803_6433429.htm.
    [10] Liu C, Wang P, Wen T, et al. (2021) Spatio-temporal characteristics of climate change in the Yellow River source area from 1960 to 2019. Arid Zone Res 38: 293–302. https://doi.org/10.13866/j.azr.2021.02.01 doi: 10.13866/j.azr.2021.02.01
    [11] Cui Y, Zhang B, Huang H, et al. (2021) Spatiotemporal Characteristics of Drought in the North China Plain over the Past 58 Years. Atmosphere 12: 844. https://doi.org/10.3390/atmos12070844 doi: 10.3390/atmos12070844
    [12] Guan Q, Ding M, Zhang H (2019) Spatiotemporal Variation of Spring Phenology in Alpine Grassland and Response to Climate Changes on the Qinghai-Tibet, China. Mt Res 37: 639–648. https://doi.org/10.16089/j.cnki.1008-2786.000455 doi: 10.16089/j.cnki.1008-2786.000455
    [13] Xie W, Yan X (2023) Responses of Wheat Protein Content and Protein Yield to Future Climate Change in China during 2041-2060. Sustainability 15: 14204. https://doi.org/10.3390/su151914204 doi: 10.3390/su151914204
    [14] Lan Y, Chawade A, Kuktaite R, et al. (2022) Climate Change Impact on Wheat Performance—Effects on Vigour, Plant Traits and Yield from Early and Late Drought Stress in Diverse Lines. Int J Mol Sci 23: 3333. https://doi.org/10.3390/ijms23063333 doi: 10.3390/ijms23063333
    [15] Yi F, Zhou T, Chen X (2021) Climate Change, Agricultural Research Investment and Agricultural Total Factor Productivity. J Nanjing Agric Univ 21: 155–167. https://doi.org/10.19714/j.cnki.1671-7465.2021.0065 doi: 10.19714/j.cnki.1671-7465.2021.0065
    [16] Gourevitch JD, Koliba C, Rizzo DM, et al. (2021) Quantifying the social benefits and costs of reducing phosphorus pollution under climate change. J Environ Manage 293: 112838. https://doi.org/10.1016/j.jenvman.2021.112838 doi: 10.1016/j.jenvman.2021.112838
    [17] Brizmohun R (2019) Impact of climate change on food security of small islands: The case of Mauritius. Nat Resour Forum 43: 154–163. https://doi.org/10.1111/1477-8947.12172 doi: 10.1111/1477-8947.12172
    [18] Cheng J, Yin S (2022) Quantitative Assessment of Climate Change Impact and Anthropogenic Influence on Crop Production and Food Security in Shandong, Eastern China. Atmosphere 13: 1160. https://doi.org/10.3390/atmos13081160 doi: 10.3390/atmos13081160
    [19] Hu J, Wang H, Song Y (2023) Spatio-Temporal Evolution and Driving Factors of "Non-Grain Production" in Hubei Province Based on a Non-Grain Index. Sustainability 15: 9042. https://doi.org/10.3390/su15119042 doi: 10.3390/su15119042
    [20] Feng Y, Ke M, Zhou T (2022) Spatio-Temporal Dynamics of Non-Grain Production of Cultivated Land in China. Sustainability 14: 14286. https://doi.org/10.3390/su142114286 doi: 10.3390/su142114286
    [21] Zhao S, Xiao D, Yin M (2023) Spatiotemporal Patterns and Driving Factors of Non-Grain Cultivated Land in China's Three Main Functional Grain Areas. Sustainability 15: 13720. https://doi.org/10.3390/su151813720 doi: 10.3390/su151813720
    [22] Li Y, Han X, Zhou B, et al. (2023) Farmland Dynamics and Its Grain Production Efficiency and Ecological Security in China's Major Grain-Producing Regions between 2000 and 2020. Land 12: 1404. https://doi.org/10.3390/land12071404 doi: 10.3390/land12071404
    [23] Liu X, Xu Y (2023) Analysis of Dynamic Changes and Main Obstacle Factors of Grain Supply and Demand Balance in Northwest China. Sustainability 15: 10835. https://doi.org/10.3390/su151410835 doi: 10.3390/su151410835
    [24] Niu Y, Xie G, Xiao Y, et al. (2021) Spatiotemporal Patterns and Determinants of Grain Self-Sufficiency in China. Foods 10: 747. https://doi.org/10.3390/foods10040747 doi: 10.3390/foods10040747
    [25] Jiang L, Wu S, Liu Y (2022) Change Analysis on the Spatio-Temporal Patterns of Main Crop Planting in the Middle Yangtze Plain. Remote Sens 14: 1141. https://doi.org/10.3390/rs14051141 doi: 10.3390/rs14051141
    [26] Wang X, Li J, Li J, et al. (2023) Temporal and Spatial Evolution of Rice Productivity and Its Influencing Factors in China. Agronomy 13: 1075. https://doi.org/10.3390/agronomy13041075 doi: 10.3390/agronomy13041075
    [27] Zeng X, Li Z, Zeng F, et al. (2023) Spatiotemporal Evolution and Antecedents of Rice Production Efficiency: From a Geospatial Approach. Systems 11: 131. https://doi.org/10.3390/systems11030131 doi: 10.3390/systems11030131
    [28] Wen F, Lyu D, Huang D (2023) Spatiotemporal Heterogeneity of Total Factor Productivity of Grain in the Yangtze River Delta, China. Land 12: 1476. https://doi.org/10.3390/land12081476 doi: 10.3390/land12081476
    [29] Bao B, Jiang A, Jin S, et al. (2021) The Evolution and Influencing Factors of Total Factor Productivity of Grain Production Environment: Evidence from Poyang Lake Basin, China. Land 10: 606. https://doi.org/10.3390/land10060606 doi: 10.3390/land10060606
    [30] Xu H, Ma B, Gao Q (2021) Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China. Front Env Sci 9: 774343. https://doi.org/10.3389/fenvs.2021.774343 doi: 10.3389/fenvs.2021.774343
    [31] Luo J (2019) Study on the impact of climate change on grain crop yields in the last 20 years—Based on Guiyang city region data. Grain Sci Technol Econ 44: 36–40. https://doi.org/10.16465/j.gste.cn431252ts.20190705 doi: 10.16465/j.gste.cn431252ts.20190705
    [32] Zhu B, Hu X, Zhou Q, et al. (2014) The Impact of Climate Change on Grain Production in Qihe County and Countermeasures. J Anhui Agric Sci 28: 9869–9871. https://doi.org/10.13989/j.cnki.0517-6611.2014.28.084 doi: 10.13989/j.cnki.0517-6611.2014.28.084
    [33] Wu H, Yu X, Tian T (2019) Impact of heat resources on crop production in Siping region. Agric Jilin 2019: 106. https://doi.org/10.14025/j.cnki.jlny.2019.06.059 doi: 10.14025/j.cnki.jlny.2019.06.059
    [34] Zhu X, Yang Y, Hu B (1999) Impacts of climate warming on agriculture in Huoijia County and countermeasures. Meteorol J Henan 1999: 33. https://doi.org/10.16765/j.cnki.1673-7148.1999.01.024 doi: 10.16765/j.cnki.1673-7148.1999.01.024
    [35] Liu Y, Liu Y, Guo L (2010) Impact of climatic change on agricultural production and response strategies in China. Chinese J Eco-Agric 18: 905–910. https://doi.org/10.3724/SP.J.1011.2010.00905 doi: 10.3724/SP.J.1011.2010.00905
    [36] Su F, Liu Y, Wang S, et al. (2022) Impact of climate change on food security in different grain producing areas in China. China Popul Resour Environ 32: 140–152. https://doi.org/10.12062/cpre.20220515 doi: 10.12062/cpre.20220515
    [37] Liu L, Liu X, Lun F, et al. (2018) Research on China's Food Security under Global Climate Change Background. J Nat Resour 33(6): 927–939. https://doi.org/10.31497/zrzyxb.20180436 doi: 10.31497/zrzyxb.20180436
    [38] Chou J, Dong W, Xu H, et al. (2022) New Ideas for Research on the Impact of Climate Change on China's Food Security. Clim Environ Res 27: 206–216. https://doi.org/10.3878/j.issn.1006-9585.2021.21148 doi: 10.3878/j.issn.1006-9585.2021.21148
    [39] Xu Y, Chou J, Yang F, et al. (2021) Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China. Atmosphere 12: 172. https://doi.org/10.3390/atmos12020172 doi: 10.3390/atmos12020172
    [40] Chou J, Xu Y, Dong W, et al. (2019) Comprehensive climate factor characteristics and quantitative analysis of their impacts on grain yields in China's grain-producing areas. Heliyon 5: e2846. https://doi.org/10.1016/j.heliyon.2019.e02846 doi: 10.1016/j.heliyon.2019.e02846
    [41] Chou J, Xu Y, Dong W, et al. (2019) Research on the variation characteristics of climatic elements from April to September in China's main grain-producing areas. Theor Appl Climatol 137: 3197–3207. http://doi.org/10.1007/s00704-019-02795-y doi: 10.1007/s00704-019-02795-y
    [42] Baike (2023) China Central Region - Six Provinces in the Central Region of China. 360 Baike. Available from: https://upimg.baike.so.com/doc/6844931-32332251.html.
    [43] Xinhua News Agency (2011) According to the approval of the State Council, Anhui abolished the prefecture-level Chaohu City: the establishment of county-level city. The Central People's Government of the People's Republic of China. Available from: https://www.gov.cn/jrzg/2011-08/22/content_1929919.htm.
    [44] Surface meteorological observations in China. China Meteorological Data Service Centre: National Meteorological Information Centre. Available from: https://data.cma.cn/data/detail/dataCode/A.0012.0001.S011.html.
    [45] Huang X, Gong P, White M (2022) Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai. Int J Environ Res Public Health 19: 7929. https://doi.org/10.3390/IJERPH19137929 doi: 10.3390/IJERPH19137929
    [46] Wang L, Xu J, Liu Y, et al. (2024) Spatial Characteristics of the Non-Grain Production Rate of Cropland and Its Driving Factors in Major Grain-Producing Area: Evidence from Shandong Province, China. Land 13: 22. https://doi.org/10.3390/LAND13010022 doi: 10.3390/LAND13010022
    [47] Xu J, Liao W, Fong CS (2023) Identification and simulation of traffic crime risk posture within the central city of Wuhan in China. SPIE - The International Society for Optical Engineering 12797: 1279712. https://doi.org/10.1117/12.3007821 doi: 10.1117/12.3007821
    [48] Lai X, Gao C (2023) Spatiotemporal Patterns Evolution of Residential Areas and Transportation Facilities Based on Multi-Source Data: A Case Study of Xi'an, China. ISPRS Int J Geo-Inf 12: 233. https://doi.org/10.3390/ijgi12060233 doi: 10.3390/ijgi12060233
    [49] Fu WJ, Jiang PK, Zhou GM, et al. (2014) Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences 11: 2401–2409. https://doi.org/10.5194/BG-11-2401-2014 doi: 10.5194/BG-11-2401-2014
    [50] Sun J, Fan P, Wang K, et al. (2022) Research on the Impact of the Industrial Cluster Effect on the Profits of New Energy Enterprises in China: Based on the Moran's I Index and the Fixed-Effect Panel Stochastic Frontier Model. Sustainability 14: 14499. https://doi.org/10.3390/SU142114499 doi: 10.3390/SU142114499
    [51] Zhou T, Niu A, Huang Z, et al. (2020) Spatial Relationship between Natural Wetlands Changes and Associated Influencing Factors in Mainland China. ISPRS Int J Geo-Inf 9: 179. https://doi.org/10.3390/IJGI9030179 doi: 10.3390/IJGI9030179
    [52] Shan Y, Wang N (2023) Spatiotemporal Evolution and the Influencing Factors of China's High-Tech Industry GDP Using a Geographical Detector. Sustainability 15: 16678. https://doi.org/10.3390/SU152416678 doi: 10.3390/SU152416678
    [53] Anselin L (1995) Local Indicators of Spatial Association—LISA. Geogr Anal 27: 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x doi: 10.1111/j.1538-4632.1995.tb00338.x
    [54] Wen J, Zhang C, Zhang L, et al. (2020) Spatiotemporal Evolution and Influencing Factors of Chinese Grain Production under Climate Change. J Henan Univ 50: 652–665. https://doi.org/10.15991/j.cnki.411100.2020.06.003 doi: 10.15991/j.cnki.411100.2020.06.003
    [55] Qin Z, Tang H, Li W (2015) Front Issues in Studying the impacts of climate change on grain farming system in China. Chin J Agric Resour Reg Plann 36: 1–8. https://doi.org/10.7621/cjarrp.1005-9121.20150101 doi: 10.7621/cjarrp.1005-9121.20150101
    [56] Mitchell A, Griffin LS (2021) The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, second edition. ESRI Press. https://www.esri.com/en-us/esri-press/browse/the-esri-guide-to-gis-analysis-volume-2-spatial-measurements-and-statistics-second-edition
    [57] Wang K, Cai H, Yang X (2016) Multiple scale spatialization of demographic data with multi-factor linear regression and geographically weighted regression models. Prog Geogr 35: 1494–1505. https://doi.org/10.18306/dlkxjz.2016.12.006 doi: 10.18306/dlkxjz.2016.12.006
    [58] People's Daily (2023) There was a net increase of about 1.3 million mu of arable land in the country last year. The Central People's Government of the People's Republic of China. Available from: https://www.gov.cn/yaowen/2023-04/17/content_5751795.htm.
    [59] Zhai P, Yu R, Guo Y, et al. (2016) The strong El Niñ o in 2015/2016 and its dominant impacts on global and China's climate. Acta Meteorol Sin 74: 309–321. http://doi.org/10.11676/qxxb2016.049 doi: 10.11676/qxxb2016.049
    [60] Zhou S (2015) Safeguarding food production against climate change needs urgent measures. China Dialogue. Available from: https://chinadialogue.net/en/climate/7660-safeguarding-food-production-against-climate-change-needs-urgent-measures/.
    [61] Climate Change Research Laboratory (2014) Climate change and food security. Institute of Environment and Sustainable Development in Agriculture, CAAS. Available from: https://ieda.caas.cn/xwzx/kyjz/259169.htm.
    [62] CPPCC Daily (2022) Commissioner Dingzhen Zhu: the impact of climate change on China's food security cannot be ignored. The National Committee of the Chinese People's Political Consultative Conference. Available from: http://www.cppcc.gov.cn/zxww/2022/04/29/ARTI1651201384104198.shtml.
    [63] Liang B (2010) Chinese Academy of Agricultural Sciences actively explores the impacts of climate change on China's food production system and its adaptation mechanisms. Ministry of Agriculture and Rural Affairs of the People's Republic of China. Available from: http://www.moa.gov.cn/xw/zwdt/201009/t20100908_1652968.htm.
    [64] Farmers' Daily (2023) As climate change and extreme weather increase, how to ensure food production security? — Conversations with Haitao Lan, Shengdou Chen, and Juqi Duan. Chongqing Agriculture and Rural Committee. Available from: https://nyncw.cq.gov.cn/zwxx_161/rdtt/202309/t20230908_12318096_wap.html.
    [65] Qi M (2023) How are China's mountain farmers adapting to climate change? World Economic Forum. Available from: https://cn.weforum.org/agenda/2023/09/how-chinese-mountain-farmers-adapt-to-climate-change/.
  • geosci-10-03-024-s001.pdf
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1714) PDF downloads(116) Cited by(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog