Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Maturity level of environmental management in the pulp and paper supply chain

  • This research aimed to identify the level of maturity in environmental management in a focal company of a pulp and paper supply chain. Methodologically, it is characterized as a qualitative exploratory case study. Semi-structured interviews were used to collect the data. The adoption and use of Environmental Management Supply Chain (ESCM) practices was assessed using a model based on 53 practices grouped into 8 types of practices. Qualitative data analysis software (NVivo) was used to analyse the data and support the development of findings. It was found that 85% of the ESCM practices were adopted by the company. Internal environmental management practices, waste and risk minimization and eco-design were fully adopted. Furthermore, a proactive maturity level was found, embedded in the company's strategic planning. Proactivity in environmental management encourages continuous improvement, cost reduction, cleaner production, and reuse and recycling of products.

    Citation: Antonio Zanin, Ivonez Xavier de Almeida, Francieli Pacassa, Fabricia Silva da Rosa, Paulo Afonso. Maturity level of environmental management in the pulp and paper supply chain[J]. AIMS Environmental Science, 2021, 8(6): 580-596. doi: 10.3934/environsci.2021037

    Related Papers:

    [1] Ronald Kamoga, Godfrey Zari Rukundo, Samuel Kalungi, Wilson Adriko, Gladys Nakidde, Celestino Obua, Johnes Obongoloch, Amadi Ogonda Ihunwo . Vagus nerve stimulation in dementia: A scoping review of clinical and pre-clinical studies. AIMS Neuroscience, 2024, 11(3): 398-420. doi: 10.3934/Neuroscience.2024024
    [2] Arosh S. Perera Molligoda Arachchige . Transitioning from PET/MR to trimodal neuroimaging: why not cover the temporal dimension with EEG?. AIMS Neuroscience, 2023, 10(1): 1-4. doi: 10.3934/Neuroscience.2023001
    [3] Craig T. Vollert, Jason L. Eriksen . Microglia in the Alzheimers brain: a help or a hindrance?. AIMS Neuroscience, 2014, 1(3): 210-224. doi: 10.3934/Neuroscience.2014.3.210
    [4] Ubaid Ansari, Vincent Chen, Romteen Sedighi, Burhaan Syed, Zohaer Muttalib, Khadija Ansari, Fatima Ansari, Denise Nadora, Daniel Razick, Forshing Lui . Role of the UNC13 family in human diseases: A literature review. AIMS Neuroscience, 2023, 10(4): 388-400. doi: 10.3934/Neuroscience.2023029
    [5] Marco Calabrò, Carmela Rinaldi, Giuseppe Santoro, Concetta Crisafulli . The biological pathways of Alzheimer disease: a review. AIMS Neuroscience, 2021, 8(1): 86-132. doi: 10.3934/Neuroscience.2021005
    [6] Adi Wijaya, Noor Akhmad Setiawan, Asma Hayati Ahmad, Rahimah Zakaria, Zahiruddin Othman . Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA). AIMS Neuroscience, 2023, 10(2): 154-171. doi: 10.3934/Neuroscience.2023012
    [7] Kiran Kumar Siddappaji, Shubha Gopal . Molecular mechanisms in Alzheimer's disease and the impact of physical exercise with advancements in therapeutic approaches. AIMS Neuroscience, 2021, 8(3): 357-389. doi: 10.3934/Neuroscience.2021020
    [8] Frank O. Bastian . Is Alzheimers Disease Infectious?
    Relative to the CJD Bacterial Infection Model of Neurodegeneration. AIMS Neuroscience, 2015, 2(4): 240-258. doi: 10.3934/Neuroscience.2015.4.240
    [9] Nour Kenaan, Zuheir Alshehabi . A review on recent advances in Alzheimer's disease: The role of synaptic plasticity. AIMS Neuroscience, 2025, 12(2): 75-94. doi: 10.3934/Neuroscience.2025006
    [10] Danton H. O’Day . Alzheimer’s Disease: A short introduction to the calmodulin hypothesis. AIMS Neuroscience, 2019, 6(4): 231-239. doi: 10.3934/Neuroscience.2019.4.231
  • This research aimed to identify the level of maturity in environmental management in a focal company of a pulp and paper supply chain. Methodologically, it is characterized as a qualitative exploratory case study. Semi-structured interviews were used to collect the data. The adoption and use of Environmental Management Supply Chain (ESCM) practices was assessed using a model based on 53 practices grouped into 8 types of practices. Qualitative data analysis software (NVivo) was used to analyse the data and support the development of findings. It was found that 85% of the ESCM practices were adopted by the company. Internal environmental management practices, waste and risk minimization and eco-design were fully adopted. Furthermore, a proactive maturity level was found, embedded in the company's strategic planning. Proactivity in environmental management encourages continuous improvement, cost reduction, cleaner production, and reuse and recycling of products.



    In much of the literature, time fractional models are defined using the Caputo definition [32,33,34,35,36], in which time fractional models are models described by fractional differential equations or pseudo state space descriptions. The Caputo definition is widely acclaimed because it makes it possible to define initial conditions that relate to the integer derivatives of the derived functions in the models considered. However, this paper shows that this definition does not take initial conditions properly into account if used to define a time fractional model.

    The problem was analysed for the first time by Lorenzo and Hartley [1,2]. To take the past of the model into account in a convenient way in a finite interval, they introduced an initialization function. The idea of replacing the commonly used initial values by an initial function was further developed in [3]. In [4], the need to consider the "prehistories" before the initial instant of the derivate functions was shown, making it possible to address the initialisation of fractional visco-elastic equations to reach a unique solution. In [5,6], a counter example was used to demonstrate that initial conditions cannot be correctly taken into account in a dynamical model whether by Caputo or Riemann-Liouville definitions. This led to the conclusion in [7] that fractional derivative and time fractional model initializations are two distinct problems. Still using an initial time shifting method, counter examples were proposed in [8] to show similar initialisation problems with the Caputo definition for partial differential equations. A time shifting technique was also recently used in [9] to analyse a groundwater flow model with time Caputo or Riemann-Liouville fractional partial derivatives. The non-objectivity of these models was demonstrated in this paper. The authors in [9] did not address the problem of initialization, but this objectivity can be restored by also introducing an initialization function (instead of initial conditions).

    As previously mentioned, several studies and several solutions have already been published on initialisation of fractional models, but many papers in which the initial conditions are taken into account incorrectly are also still published. Thus the novelties and the contributions of the paper are new demonstrations and new simulations that highlight how initialisations must be done with a time fractional model. Thus, in this paper, two examples are used to show that the Caputo definition does not enable initial conditions to be correctly handled when this definition is used to define a time fractional model. In the first example, the response of a simple model, assumed to be at rest, is calculated analytically on a given time interval. Then inside this interval, a second response is computed by considering initial conditions resulting from the first simulation, and ignoring the model past before the considered initial time. This is the initialisation currently found in the literature and this example shows that it is unable to ensure the correct model trajectories. In the second example, two different histories are generated that produce the same initial conditions for the model. This example shows that in spite of equal initial conditions, the model response is different, thus showing that all the model past must be taken into account to define its future. A similar analysis is also carried out with the Riemann-Liouville and the Grünwald-Letnikov's definitions, suggesting that other definitions should also be problematic. Note that all the analyzes carried out and conclusions obtained in this paper relate to models involving only time fractional derivatives and not space fractional derivatives as in [29,30,31].

    The fractional integral of order ν, 0<ν<1, of a function y(t) is defined by [10]:

    Iνt0y(t)=1Γ(ν)tt0y(τ)(tτ)1νdτ. (1)

    Γ(.) being Euler's gamma function. From this definition, the Caputo derivative definition of order ν, 0<ν<1, of a function y(t) is defined by [11]:

    CDνt0y(t)=I1νt0(ddty(t))=1Γ(1ν)tt01(tτ)νdy(τ)dτdτ. (2)

    Laplace transform applied to relation (2) reveals how initial conditions are associated to this definition:

    L{CDνt0y(t)}=1s1ν(sY(s)y(t0)) = sνY(s)y(t0). (3)

    To demonstrate that Caputo definition is not able to take initial conditions correctly into account when used to define a time fractional model (a fractional differential equation or a pseudo state space description), the following model is considered

    Dνy(t)=ay(t)+u(t)  0<ν<1  a>0. (4)

    In relation (4), Dν denotes the Caputo definition in this section but denotes the Riemann-Liouville or Grünwald-Letnikov definitions in the next section. Then, the following algorithm is used to study model (4).

    Algorithm 1

    1-Simulation on the time interval [0,t1] of the time fractional model (for instance model (4)) with null initial conditions (for t],0]). Let S1 denote this simulation.

    2-Record the model output y(t) and the integer derivatives of y(t) (denoted y'(t), y''(t), ….) at time t0 such that 0<t0<t1.

    3-Simulate the model again on [t0,t1], using y(t0), y'(t0), y''(t0) … as initial conditions. Let S2 denote this simulation.

    4-Compare S1 and S2 on [t0,t1] and notice if they are different.

    Algorithm 1 is now applied to model (4) with a=0. The model is assumed to be at rest before t=0, and the input u(t) is assumed to be a Heaviside function H(t). In such conditions, relation (4) is equivalent to [11]

    y(t)=y(t0)+Iνt0{H(t)}  0<ν<1. (5)

    As a consequence, the simulation defined in Algorithm 1 provides the following solutions:

    S1:y(t)=tνΓ(ν+1)  0<t<t1 (6)
    S2:y(t)=(tt0)νΓ(ν+1)+y(t0)  t0<t<t1. (7)

    Figure 1 proposes a comparison of S1 and S2 and reveals a difference, thus demonstrating that the Caputo definition does not correctly take initial conditions into account.

    Figure 1.  Comparison of the exact response of model (4) with the responses obtained with Caputo definitions with initial conditions (t0=5s, a=0, ν=0.6).

    Another way to illustrate this result is to consider two different input signals u1(t) and u2(t) that create two different histories with:

    ui(t)=AiH(t+ti)AiH(t)  with  ti>0,  i={1,2}. (8)

    The model is assumed to be at rest on t],ti]. A constraint is also imposed on these signals so that at t=0, the two resulting model outputs coincide:

    y1(0)=y2(0). (9)

    The output yi(t) is thus defined by:

    yi(t)=Aia(1E1ν,1(a(t+ti)ν))H(t+ti)Aia(1E1ν,1(atν))H(t). (10)

    where Eγα,β(z) is the Mittag-Leffler function defined by [12]:

    Eγα,β(z)=k=0Γ(γ+k)Γ(αk+β)Γ(γ)zkk!. (11)

    Condition (9) thus leads to

    A1a[(1E1ν,1(a(t1)ν))(1E1ν,1(0))]=A2a[(1E1ν,1(a(t2)ν))(1E1ν,1(0))] (12)

    thus leading to the condition:

    A1=A21E1ν,1(a(t2)ν)1E1ν,1(a(t1)ν). (13)

    With ν=0.4, a=1, t1=8s, t2=2s, A2=5 and thus A14.17, Figure 2 shows the signal inputs u1(t) and u2(t) used for the analysis and proposes a comparison of the resulting outputs. This figure shows that the two responses have the same values at t=0, but that the evolutions for t>0 are not the same. The information at t=0 is thus not enough to predict the future of the model. All the past must be taken into account to predict the future of the model, which confirms that initialization as defined by the Caputo definition is not acceptable if used to define a time fractional model such as (4).

    Figure 2.  Comparison of the responses y1(t) and y2(t) of model (4) to two inputs that provide the same initial conditions.

    The previous section showed that the Caputo definition should no longer be used to define time fractional models such as (4). What about other definitions?

    The Riemann-Liouville derivative of order ν, 0<ν<1, of a function y(t) is defined by [11]:

    RLDνt0y(t)=1Γ(1ν)ddttt0y(τ)(tτ)νdτ. (14)

    Laplace transform applied to relation (14) reveals how initial conditions are associated to this definition:

    L{RLDνt0y(t)}=L{ddt(1Γ(1ν)tt0y(τ)(tτ)νdτ)}=s1s1νY(s)[I1νt0y(t)]t=t0. (15)

    As a consequence, in [11,13], the initialisation of relation (4) is defined by

    dνdtνy(t)=ay(t)+u(t)  I1νt0{y(t)}|t=t0=y0 (16)

    and thus the initialisation problem of relation (4) is equivalent to the integral equation

    y(t)=y0Γ(ν)(tt0)ν1+Iνt0{ay(t)+u(t)}. (17)

    Algorithm 1 is applied again to model (4) with a=0. The model is assumed to be at rest before t=0, and the input u(t) is assumed to be a Heaviside function H(t). Algorithm 1 provides the following solutions:

    S1:y(t)=tνΓ(ν+1)  0<t<t1 (18)
    S2:y(t)=(tt0)νΓ(ν+1) + y0Γ(ν)(tt0)ν1  t0<t<t1. (19)

    Relation (19) seems to say that any value of y0 can be chosen, but whatever the value selected, for S2 y(t) tends toward infinity as t tends toward t0 if y00 whereas y(t)=t0ν/Γ(ν+1) for S1. The two simulations thus give different results. This is illustrated by Figure 3 for various values of y0.

    Figure 3.  Comparison of the exact response of model (4) with the responses obtained with the Riemann-Liouville definition (t0 = 5s, a=0,ν=0.7).

    The Grünwald-Letnikov derivative of order ν, 0<ν<1, of a function y(t) is defined by:

    GLDνt0y(t)=h0_lim1hν0m<(1)m(νm)f(tmh)  t>t0 (20)

    with (νm)=Γ(ν+1)m!Γ(νm+1)=ν(ν1)(ν2)(νm+1)m(m1)(m2)(mm+1).

    This definition is often used in the literature as it provides a simple numerical scheme for fractional derivative implementation. In some research [14,15,16], these numerical schemes are used to solve the initialisation problem:

    Dvt0y(t)=ay(t)+u(t)0<v<1a>0 for t0<v<T, (21)
    y(t0)=y0.

    In this case, it is not the Grünwald-Letnikov derivative definition which is questionable, but the idea that a time fractional model can be initialized solely with information on the initial moment. From relation (20), it is possible to observe that variable m goes from 0 to infinity, and thus this definition is able to take into account the past of the derivative function, prior to t0. In (21), the problem is the way the initial conditions are defined.

    To illustrate this problem, Algorithm 1 is applied to model (4) with a=1. The model is assumed to be at rest before t=0, and the input u(t) is assumed to be a Heaviside function H(t). In such conditions, the simulation S1 defined in Algorithm 1 provides the following solution:

    S1:y(t)=tνΓ(ν+1)  0<t<t1. (22)

    Simulation S2 is done using the Grünwald-Letnikov formula (20) and provides

    S2:y(t)=1hν1m<(1)m(νm)y(tmh)+H(t)1hν+1  t>t0. (23)

    This simulation is done under two conditions:

    - S21: by taking into account all the past of the model (all the values of y(t) on t[0,t0], provided by S1)

    - S22: by considering only an initial condition at t0 (value of y(t) at t0 provided by S1).

    The comparison of the three simulations is done in Figure 4 and reveals that the Grünwald-Letnikov definition produces an exact solution provided that all the past of the model is taken into account.

    Figure 4.  Comparison of the exact response of model (4) with the responses obtained with the Grünwald-Letnikov definition (t0 = 3s, a=1,ν=0.6).

    Relation (23) is particularly interesting because it shows that a time fractional model (here a fractional integrator) is represented by an infinite difference equation, and therefore an initialization of all its terms is necessary for a prediction of the output y(t).

    This remark could also apply to the Caputo and Riemann-Liouville definitions which would lead to their reformulations with integrals on the interval ],t] as suggested in [27],

    The need to take into account the all past of a time fractional model and not just the knowledge of its pseudo state at a single point in the past can be demonstrated quite simply on relation (4) (a particular case of fractional differential equation or of pseudo state space description). Contrary to what relation (4) might suggest, Figure 5 highlights that the implementation of fractional differential equations does not explicitly involve the fractional differentiation operator but the fractional order integration operator Iν. Thus in practice, it is not necessary to specify which particular definition is used for Dν in equation (4). Moreover, even if the system is assumed to have zero initial conditions at t=0, namely if the system is supposed at rest (u(t)=y(t)=0, t<0), it is important to note that y(t) cannot be considered as a state for the time fractional model and that all the past of y(t) is required to compute the model evolution.

    Figure 5.  Block diagram Eq (4).

    To better illustrate such a concept, a simple time fractional model is used: a fractional integrator supposed at rest at t=0. The corresponding block diagram is shown in Figure 6.

    Figure 6.  Block diagram of an order ν fractional integrator.

    For an integer integrator, ν=1, relation (4) is really a state space description. At t1>0, state y(t) can be computed if the input between 0 and t1 is known:

    y(t1)=t10x_(τ)dτ=y1=cst. (24)

    Values of y(t) at later times than t1 are given by:

    y(t)=t0x_(τ)dτ=t10x_(τ)dτy1=cst+tt1x_(τ)dτ,t>t1. (25)

    Thus, y(t) can be computed if x_(t) is known within t1 and t. Integrator output at time t1 thus summarizes the whole model past. y(t) is really the state of the dynamic model, in agreement with the definition given in [26].

    Let us apply the same reasoning to the fractional integrator case of order ν. From the definition of fractional integration, value of y(t) at t1>0 can be computed if the input between t=0 and t1 is known:

    y(t1)=1Γ(ν)t10(t1τ)ν1x_(τ)dτ=y1=cst. (26)

    Variable y(t), t>t1, is thus given by:

    y(t1)=1Γ(v)t0(tτ)v1x_(τ)dτ=1Γ(v)t10(tτ)v1x_(τ)dτα(t)y1+1Γ(v)tt1(tτ)v1x_(τ)dτ. (27)

    Two notable differences can be highlighted with respect to the integer case. First, term α(t) in equation (27) is not a constant but depends on the considered time t. Moreover, even if y1=y(t1) is known, it is not enough to compute α(t). Output y(t) of the fractional integrator is thus not a state. The same analysis can be held for the general case of a pseudo state description or a fractional differential equation.

    Beyond discussions on the concept of state, computation of α(t) in relation (27) whatever time t, requires to know y(t) t[0..t1], thus all the model past. This clearly shows that knowledge of y(t) at a unique point of the past is not enough.

    Fractional operators and the resulting time fractional models are known for their memory property. However, for the following two reasons, many studies proposed in the literature seem to ignore this property when the model initialization problem is considered:

    -they use the Caputo definition that involves only integer derivatives of the derivate function at the initial time,

    -they use other definitions but initialization is done by taking only an initial value for the initial time into consideration.

    This kind of initialization means that the operator or model memory exists everywhere on the time axis, except at the initial time. This is not consistent. Memory is an intrinsic property that exists all the time and that is proved in this paper with very simple examples. If from a mathematical point of view, most of the fractional derivative definitions encountered in the literature [17] are not problematic, this paper shows that the Caputo and Riemann-Liouville definitions are not able to ensure a proper initialization when used in a model definition. The paper also shows that this problem is not encountered with the Grünwald-Letnikov definition, provided that all the past of the model (from t) is taken into account. And this is precisely one of the drawbacks of time fractional models that induces a physical inconsistency and many analysis problems [17].

    What are the possible solutions? One solution can be to add an initialization function to the definition of the model. This is what was proposed by Lorenzo and Hartley [1,2]. Yet again however, it requires the knowledge of all the model past (from t). Another solution consists in introducing new kernels for the definition of fractional integration as in [19]. But the goal would not be to solve only a singularity problem as in [19], but to reach a finite memory length as was done for instance in [20]. Note that while it was claimed in [21] that this class of kernels was too restrictive, it is linked to the problem analyzed in this paper: the inability of the Caputo definition to take into account initial conditions properly if used to define a time fractional model [22]. The other solution is to introduce new solutions for fractional behavior modeling, without the drawbacks associated to time fractional models [18]:

    -distributed time delay models [23];

    -non-linear models [24];

    -partial differential equation with spatially varying coefficients [25].

    All the conclusions presented in this paper can they be extended to models involving space fractional derivatives as in [29,30,31]? As shown in [28], whatever the variable on which the derivative relates, a fractional model remains a doubly infinite dimensional model and as such requires an infinite amount of information for its initialization. The question remains open, however, the authors will seek to answer it in their future work.



    [1] Ferreira MA, Jabbour CJC, Jabbour ABLS (2017) Maturity levels of material cycles and waste management in a context of green supply chain management: An innovative framework and its application to Brazilian cases. J Mater Cycles Waste 19: 516-525. doi: 10.1007/s10163-015-0416-5
    [2] Potrich L, Cortimiglia MN, Medeiros JF (2019) A systematic literature review on firm-level proactive environmental management. J Environ Manage 243: 273-286. doi: 10.1016/j.jenvman.2019.04.110
    [3] Koberg E, Longoni A (2019) A systematic review of sustainable supply chain management in global supply chains. J Clean Prod 207: 1084-1098. doi: 10.1016/j.jclepro.2018.10.033
    [4] Green KW, Zelbst PJ, Meacham J, et al. (2012) Green supply chain management practices: impact on performance. Supply Chain Manag 17: 290-305. doi: 10.1108/13598541211227126
    [5] Zhu Q, Sarkis J, Lai K (2007) Green supply chain management: pressures, practices and performance within the Chinese automobile industry. J Clean Prod 15: 11-12, 1041-1052.
    [6] Sharma VK, Chandna P, Bhardwaj A (2017) Green supply chain management related performance indicators in agro industry: A review. J Clean Prod 141: 1194-1208. doi: 10.1016/j.jclepro.2016.09.103
    [7] Shultz CJ, Holbrook MB (1999) Marketing and the tragedy of the commons: A synthesis, commentary, and analysis for action. J Public Policy Mar 18: 218-229. doi: 10.1177/074391569901800208
    [8] Rao P, Holt D (2005) Do green supply chains lead to competitiveness and economic performance? Int J Oper Prod Man 25: 898-916. doi: 10.1108/01443570510613956
    [9] Jain VK, Sharma S (2014) Drivers Affecting the Green Supply Chain Management Adaptation: A Review. IUP J Oper Manag 13: 54-63.
    [10] Camargo TF, Zanin A, Mazzioni S, et al. (2018) Sustainability indicators in the swine industry of the Brazilian State of Santa Catarina. Environ Dev Sustain 20: 65-81. doi: 10.1007/s10668-018-0147-6
    [11] Srivastava SK (2007) Green supply-chain management: a state-of-the-art literature review. Int J Manag Rev 9: 53-80. doi: 10.1111/j.1468-2370.2007.00202.x
    [12] Darnall N, Jolley GJ, Handfield R (2008) Environmental management systems and green supply chain management: complements for sustainability? Bus Strat Environ 17: 30-45. doi: 10.1002/bse.557
    [13] Wu GC, Ding JH, Chen PS (2012) The effects of GSCM drivers and institutional pressures on GSCM practices in Taiwan's textile and apparel industry. Int J Prod Econ 135: 618-636. doi: 10.1016/j.ijpe.2011.05.023
    [14] Jabbour ABLS, Jabbour CJC, Latan H, et al (2014) Quality management, environmental management maturity, green supply chain practices and green performance of Brazilian companies with ISO 14001 certification: Direct and indirect effects. Transport Research E-Log 67: 39-51. doi: 10.1016/j.tre.2014.03.005
    [15] Maialle G, Jabbour ABLS, Arantes AF, et al. (2016) Environmental management maturity of local and multinational high-technology corporations located in Brazil: the role of business internationalization in pollution prevention. Production 26: 488-499. doi: 10.1590/0103-6513.176914
    [16] Ferreira MA, Jabbour CJC (2019) Relating maturity levels in environmental management by adopting Green Supply Chain Management practices: Theoretical convergence and multiple case study. Gestão e Produção 26: 1-17.
    [17] Gunarathne N, Lee KH (2019) Institutional pressures and corporate environmental management maturity. Manag Environ Qual: An Int J 30: 157-175. doi: 10.1108/MEQ-02-2018-0041
    [18] Ormazabal M, Sarriegi JM, Rich E, et al. (2020) Environmental Management Maturity: The Role of Dynamic Validation. Organ Environ 34: 145-170. doi: 10.1177/1086026620929058
    [19] Pimenta HCD, Ball PD (2015) Analysis of environmental sustainability practices across upstream supply chain management. Procedia Cirp 26: 677-682. doi: 10.1016/j.procir.2014.07.036
    [20] Costa Filho BA, Rosa F (2017) Maturidade em gestão ambiental: Revisitando as melhores práticas. Revista Eletrônica de Administração: 23: 110-134.
    [21] Jabbour CJC, Santos FCA, Nagano MS (2009) Análise do relacionamento entre estágios evolutivos da gestão ambiental e dimensões de recursos humanos: estado da arte e survey em empresas brasileiras. Revista de Administração-RAUSP 44: 342-364.
    [22] Zanin A, Dal Magro CB, Mazzioni S, et al. (2019) Triple Bottom Line Analysis in an Agribusiness Supply Chain. International Joint Conference on Industrial Engineering and Operations Management 264-273.
    [23] Zanin A, Dal Magro CB, Kleinibing Bugalho D, et al. (2020) Driving Sustainability in Dairy Farming from a TBL Perspective: Insights from a Case Study in the West Region of Santa Catarina, Brazil. Sustainability 12: 6038-6056. doi: 10.3390/su12156038
    [24] Jabbour CJC (2007) Contribuições da gestão de recursos humanos para a evolução da gestão ambiental empresarial: survey e estudo de múltiplos casos. Universidade de São Paulo.
    [25] Sheu JB, Chou YH, Hu CC (2005) An integrated logistics operational model for green-supply chain management. Transport Research E-Log 41: 287-313. doi: 10.1016/j.tre.2004.07.001
    [26] Beamon BM (1999) Designing the green supply chain. Logistics Information Management 12: 332-342. doi: 10.1108/09576059910284159
    [27] Zhu Q, Sarkis J, Geng Y (2005) Green supply chain management in China: pressures, practices and performance. Int J Oper Prod Man 25: 449-468. doi: 10.1108/01443570510593148
    [28] Barve A, Muduli K (2011) Challenges to environmental management practices in Indian mining industries. International Conference on Innovation, Management and Service IPEDR 14: 297-302.
    [29] Sarkis J (2003) A strategic decision framework for green supply chain management. J Clean Prod 11: 397-409. doi: 10.1016/S0959-6526(02)00062-8
    [30] Zhu Q, Sarkis J (2004) Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. J Oper Mana 22: 265-289. doi: 10.1016/j.jom.2004.01.005
    [31] Naslund D, Williamson S (2010) What is management in supply chain management? - a critical review of definitions, frameworks and terminology. J Manag Pol Practice 11: 11-28.
    [32] Gupta V, Abidi N, Bandyopadhayay A (2013) Supply chain management - a three dimensional framework. J Manag Res 5: 76-97. doi: 10.5296/jmr.v5i4.3986
    [33] Vachon S, Klassen R D (2008) Environmental management and manufacturing performance: The role of collaboration in the supply chain. Int J Prod Econ 111: 299-315. doi: 10.1016/j.ijpe.2006.11.030
    [34] Eltayeb TK, Zailani S, Ramayah T (2011) Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes. Res Conserv Recy 55: 495-506. doi: 10.1016/j.resconrec.2010.09.003
    [35] Sulistio J, Rini TA (2015) A structural literature review on models and methods analysis of green supply chain management. Procedia Manuf 4: 291-299. doi: 10.1016/j.promfg.2015.11.043
    [36] Min H, Galle WP (1997) Green purchasing strategies: trends and implications. Int J Purch Mater Manag 33: 10-17.
    [37] Hervani AA, Helms MM, Sarkis J (2005) Performance measurement for green supply chain management. Benchmark: An Int J 12: 330-353. doi: 10.1108/14635770510609015
    [38] Kafa N, Hani Y, El Mhamedi A (2013) Sustainability performance measurement for green supply chain management. IFAC Proceedings 46: 71-78.
    [39] González BJ, González BÓ (2006) A review of determinant factors of environmental proactivity. Bus Strat Environ: 15: 87-102. doi: 10.1002/bse.450
    [40] Rehman MAA, Shrivastava RL (2011) An innovative approach to evaluate green supply chain management (GSCM) drivers by using interpretive structural modeling (ISM). Int J Innov Technol Manag 8: 315-336. doi: 10.1142/S0219877011002453
    [41] Chin TA, Tat HH, Sulaiman Z. (2015) Green supply chain management, environmental collaboration and sustainability performance. Procedia Cirp 26: 695-699. doi: 10.1016/j.procir.2014.07.035
    [42] Liu R, Zhang P, Wang X, et al. (2013) Assessment of effects of best management practices on agricultural non-point source pollution in Xiangxi River watershed. Agr Water Manage 117: 9-18. doi: 10.1016/j.agwat.2012.10.018
    [43] Jabbour ABLS, Vasquez BD, Jabbour CJC, et al. (2017) Green supply chain practices and environmental performance in Brazil: Survey, case studies, and implications for B2B. Ind Market Manag 66: 13-28. doi: 10.1016/j.indmarman.2017.05.003
    [44] Walton SV, Handfield RB, Melnyk SA (1998) The green supply chain: integrating suppliers into environmental management processes. Int J Purchas Mater Manag 34: 2-11.
    [45] Teixeira AA, Jabbour CJC, Latan H, et al. (2019) The importance of quality management for the effectiveness of environmental management: Evidence from companies located in Brazil. Total Qual Manag Bus 30: 1338-1349. doi: 10.1080/14783363.2017.1368377
    [46] Azevedo SG, Carvalho H, Machado VC (2011) The influence of green practices on supply chain performance: A case study approach. Transport Research E-Log 47: 850-871. doi: 10.1016/j.tre.2011.05.017
    [47] Jabbour CJC (2010) Non-linear pathways of corporate environmental management: a survey of ISO 14001-certified companies in Brazil. J Clean Prod 18: 1222-1225. doi: 10.1016/j.jclepro.2010.03.012
    [48] Jabbour CJC (2015) Environmental training and environmental management maturity of Brazilian companies with ISO14001: empirical evidence. J Clean Prod 96: 331-338. doi: 10.1016/j.jclepro.2013.10.039
    [49] Jabbour CJC, Santos FCA (2006) Evolução da gestão ambiental na empresa: uma taxonomia integrada à gestão da produção e de recursos humanos. Gestão & Produção 13: 435-448.
    [50] Geng R, Mansouri SA, Aktas E (2017) The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies. Int J Prod Econ 183: 245-258. doi: 10.1016/j.ijpe.2016.10.008
    [51] Mitra S, Datta PP (2014) Adoption of green supply chain management practices and their impact on performance: an exploratory study of Indian manufacturing firms. Int J Prod Res 52: 2085-2107. doi: 10.1080/00207543.2013.849014
    [52] Zhu Q, Sarkis J, Lai K, et al. (2008) The role of organizational size in the adoption of green supply chain management practices in China. Corp Soc Respon Environ Manag 15: 322-337. doi: 10.1002/csr.173
    [53] Ninlawan C, Seksan P, Tossapol K, et al. (2010) The implementation of green supply chain management practices in electronics industry. World Congress on Engineering 2012. July 4-6, 2012. London, UK., 2182, 1563-1568.
    [54] Sharma M (2014) The role of employees' engagement in the adoption of green supply chain practices as moderated by environment attitude: An empirical study of the Indian automobile industry. Global Bus Rev 15: 4, 25-38.
    [55] Tate WL, Ellram LM, Kirchoff JF (2010) Corporate social responsibility reports: a thematic analysis related to supply chain management. J Supply Chain Manag 46: 19-44. doi: 10.1111/j.1745-493X.2009.03184.x
    [56] Henriques I, Sadorsky P (1999) The relationship between environmental commitment and managerial perceptions of stakeholder importance. Academy of Management Journal 42: 87-99.
    [57] Seuring S, Müller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16: 1699-1710. doi: 10.1016/j.jclepro.2008.04.020
    [58] Kuei C, Madu CN, Chow WS, et al. (2015) Determinants and associated performance improvement of green supply chain management in China. J Clean Prod: 95,163-173. doi: 10.1016/j.jclepro.2015.02.030
    [59] Masoumik SM, Abdul-Rashid SH, Olugu EU, et al. (2015) A strategic approach to develop green supply chains. Procedia Cirp 26: 670-676. doi: 10.1016/j.procir.2014.07.091
    [60] DiMaggio PJ, Powell WW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 48: 147-160. doi: 10.2307/2095101
    [61] Meyer JW, Rowan B (1977) Institutionalized organizations: Formal structure as myth and ceremony. Am J Sociol 83: 340-363. doi: 10.1086/226550
    [62] Alvesson M, Spicer A (2019) Neo-institutional theory and organization studies: a mid-life crisis? Organ Stud 40: 199-218. doi: 10.1177/0170840618772610
    [63] Guerreiro R, Frezatti F, Lopes AB, et al. (2005) O entendimento da contabilidade gerencial sob a ótica da teoria institucional. Organizações & Sociedade 12: 91-106.
    [64] Cunha PR, Santos V, Beuren IM (2015) Artigos de periódicos internacionais que relacionam teoria institucional com contabilidade gerencial. Perspectivas Contemporâneas 10: 1-23.
    [65] Machado da Silva CL, Fonseca VS, Crubellate JM (2005) Estrutura, agência e interpretação: elementos para uma abordagem recursiva do processo de institucionalização. RAC-Revista de Administração Contemporânea 9: 9-39. doi: 10.1590/S1415-65552005000500002
    [66] Sarkis J, Zhu Q, Lai K (2011) An organizational theoretic review of green supply chain management literature. Int J Prod Econ 130: 1-15. doi: 10.1016/j.ijpe.2010.11.010
    [67] Zhu Q, Sarkis J, Lai K (2013) Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices. J Purchas Supply Manag 19: 106-117. doi: 10.1016/j.pursup.2012.12.001
    [68] Geffen CA, Rothenberg S (2000) Suppliers and environmental innovation. Int J Oper Prod Man 20: 166-186. doi: 10.1108/01443570010304242
    [69] Zhu Q, Sarkis J, Lai K (2012) Green supply chain management innovation diffusion and its relationship to organizational improvement: An ecological modernization perspective. J Eng Technol Manage 29: 168-185. doi: 10.1016/j.jengtecman.2011.09.012
    [70] Nikolopoulou A, Ierapetritou MG (2012) Optimal design of sustainable chemical processes and supply chains: A review. Comput Chem Eng 44: 94-103. doi: 10.1016/j.compchemeng.2012.05.006
    [71] Corazza RI (2003) Gestão ambiental e mudanças da estrutura organizacional. RAE Eletrônica 2: 1-23
  • This article has been cited by:

    1. Qiming Cui, Duygu Tosun, Pratik Mukherjee, Reza Abbasi-Asl, 2024, Chapter 4, 978-3-031-72103-8, 35, 10.1007/978-3-031-72104-5_4
    2. Juan‐Juan Lu, Xiang‐Xin Xing, Jiao Qu, Jia‐Jia Wu, Mou‐Xiong Zheng, Xu‐Yun Hua, Jian‐Guang Xu, Alterations of contralesional hippocampal subfield volumes and relations to cognitive functions in patients with unilateral stroke, 2024, 14, 2162-3279, 10.1002/brb3.3645
    3. Wanbing Wang, Jinhao Lyu, Xinyu Wang, Qi Duan, Runze Li, Xiangbing Bian, Caohui Duan, Song Wang, Xinbo Xing, Xin Lou, 7T MRI in cerebrovascular disorders: From large artery abnormalities to small vessel disease, 2024, 2, 29501628, 100085, 10.1016/j.metrad.2024.100085
    4. Sandhitsu R. Das, Ademola Ilesanmi, David A. Wolk, James C. Gee, Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?, 2024, 23, 1347-3182, 367, 10.2463/mrms.rev.2024-0053
    5. Oluwatobi F. Adeyemi, Penny Gowland, Richard Bowtell, Olivier Mougin, Akram A. Hosseini, Hippocampal Subfield Volume in Relation to Cerebrospinal Fluid Amyloid‐ß in Early Alzheimer's Disease: Diagnostic Utility of 7T MRI, 2025, 32, 1351-5101, 10.1111/ene.70076
    6. Cong Chu, Tales Santini, Jr‐Jiun Liou, Ann D. Cohen, Pauline M. Maki, Anna L. Marsland, Rebecca C. Thurston, Peter J. Gianaros, Tamer S. Ibrahim, Brain Morphometrics Correlations With Age Among 350 Participants Imaged With Both 3T and 7T MRI: 7T Improves Statistical Power and Reduces Required Sample Size, 2025, 46, 1065-9471, 10.1002/hbm.70195
    7. Estelle Akl, Martin Dyrba, Doreen Görß, Julia Schumacher, Marc-André Weber, MRI for diagnosing dementia – update 2025, 2025, 1438-9029, 10.1055/a-2563-0725
  • Reader Comments
  • © 2021 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(3164) PDF downloads(122) Cited by(2)

Figures and Tables

Figures(2)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog