DOI: 10.3303/CET25120070 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 15 May 2025; Revised: 30 September 2025; Accepted: 3 October 2025 
Please cite this article as: Eisinger B., Buics L., 2025, Sustainability and Energy Efficiency in Administrative Processes: A Control Theory 
Approach with P-graph Optimization, Chemical Engineering Transactions, 120, 415-420  DOI:10.3303/CET25120070 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 120, 2025 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Bing Shen How, Viknesh Andiappan, Denny K.S. Ng, Hon Loong Lam, Petar S. Varbanov 

Copyright © 2025, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-21-2; ISSN 2283-9216 

Sustainability and Energy Efficiency in Administrative 

Processes: A Control Theory Approach with P-graph 

Optimization 

Boglárka Eisingera,*, László Buicsb 

aVehicle Industry Research Center Széchenyi István University, 1. Egyetem sq., Győr 9026, Hungary 
bDepartment of Leadership and Marketing, Vehicle Industry Research Center, Széchenyi István University, 1. Egyetem sq., 

Győr 9026, Hungary 

eisingerne@ga.sze.hu 

As institutions seek to reduce their environmental impact, administrative processes must be optimized for energy 

and resource efficiency. This study integrates control theory with P-graph methodology to develop a structured 

framework for sustainable administrative workflows, focusing on university enrollment systems. P-graph-based 

optimization identifies minimum-energy pathways and optimal resource configurations, while Model Predictive 

Control (MPC) and nonlinear control enable real-time process adaptation under dynamic conditions. A Life Cycle 

Analysis (LCA) compares the carbon footprint of digital and paper-based workflows, evaluating IT infrastructure 

energy use versus traditional operations. Simulated control strategies support energy-efficient decision-making, 

highlighting best practices for emission reduction and operational flexibility. The result is a decision-support 

framework that embeds P-graph into a dynamic control context, guiding control strategy selection to minimize 

energy use and emissions. This scalable approach supports sustainability-oriented process management 

across public service domains. 

1. Introduction 

Faced with climate change, public administrations increasingly aim to make their processes more sustainable 

and energy efficient. Paper use, energy consumption, and travel contribute significantly to CO₂ emissions 

(Cambaz et al., 2018). Digital alternatives—such as paperless solutions and e-government—can reduce the 

carbon footprint (Yi, 2025), but real impact depends on thoughtful system design. An Italian LCA study showed 

that digitisation reduced transport- and paper-related environmental impacts by 55–99 % (Mirabella et al., 2013). 

Digitisation can be sustainable only if the IT infrastructure is energy-efficient and secure, as Yousufi (2023) 

emphasizes, the irresponsible or excessive use of digital technologies—such as redundant data storage and 

inefficient digital workflows—can significantly increase the carbon footprint, thereby undermining the 

environmental benefits of paperless operations. Reducing waste and simplifying processes is another pillar of 

sustainable public administration. Green Activity-Based Management (ABM) enables precise measurement and 

optimisation of energy use and emissions through activity-level data (Wesumperuma et al., 2013), while the 

Five-Circle Model highlights resource and social efficiency (Bamdad et al., 2023). Inefficient processes—like 

enrolment systems causing overtime—are common in public administration. Smart space usage and lock 

technology can cut annual energy by 14 % and significantly reduce CO₂ emissions with quick payback (Mainini 

et al., 2025). Green Business Process Management (Green BPM) helps redesign workflows using 

environmental indicators such as energy or emission reduction, yet its maturity remains low in SMEs, especially 

in modelling and optimisation (Sohns et al., 2023). Combining lean principles with digitalisation and appropriate 

software (e.g., green metrics, visualisation) effectively reduces waste (Stadtländer et al., 2019). Green HR is 

also key: employee values, awareness, and competences strongly influence sustainable BPM implementation 

(Brajer-Marczak and Piwowar-Sulej, 2023). Green BPM is both a methodology and a research agenda 

(Couckuyt and Van Looy, 2019), but its integration across the BPM lifecycle is still limited—mainly to 

environmental aspects, with social and economic dimensions often overlooked (Magdaleno et al., 2016). Still, it 

415



is increasingly evident that ecological performance must complement traditional indicators like time, cost, and 

quality. Several studies (e.g. Liu and Chen (2024)) show that digitising administration and improving user 

experience enhances both efficiency and sustainability. A 2024 analysis confirms BPM’s significant positive 

impact on corporate environmental performance. LCA is suitable for evaluating environmental impacts over a 

process’s life cycle (Fritsch et al., 2022), though it is often confined to energy aspects, rarely addressing social 

or economic ones (Gbededo et al., 2018). Research suggests integrating LCA into BPM to identify the stages 

that cause the greatest burden. For example, in a case study in Slovakia, the energy retrofit of an office building 

— involving upgrades such as improved insulation, window replacement, and HVAC system modernization — 

resulted in annual energy savings of 143 GJ and cost reductions of € 6,300, with a payback period of 6–12 y 

(Teplická et al., 2024). LCA helps identify emission sources and supports environmentally informed decision-

making. Regulatory tools are also essential for sustainable operations. Predictive building control, for instance, 

can save 17–18 % of energy (Bamdad et al., 2023). Model Predictive Control (MPC) uses real-time data for 

forecasting and optimal resource use, applicable not only to buildings but also to human and Information 

Technology (IT) capacity management. Nonlinear MPC, especially with unscented Kalman filters, ensures 

stable control in complex, uncertain systems (Rathore et al., 2021).These principles can also be adapted to 

public administration systems, for example to reduce emissions in traffic management. The P-graph 

methodology was originally developed for the optimization of chemical process networks (Friedler et al., 2019), 

but has more recently been successfully applied to the design of administrative workflows. Recent studies have 

demonstrated the potential of P-graph in various administrative and public service contexts. For instance, 

Eisinger and Buics (2024) used P-graph to optimize the university enrolment process, identifying inefficiencies 

and alternative pathways that were not visible with conventional tools, resulting in improved administrative 

transparency and reduced resource use. Earlier, Tick (2013) had already demonstrated the applicability of P-

graph to administrative workflow modeling and simulation, particularly in dynamic decision environments. The 

study highlighted its usefulness in identifying bottlenecks and reconfiguring task sequences to improve overall 

process efficiency. These examples underscore the versatility of P-graph beyond its engineering origins, 

showing its ability to address the growing demand for transparency, efficiency, and sustainability in 

administrative systems. It can generate optimal and alternative solutions by mapping the entire search space of 

possible process structures. It also can be a useful tool in the planning of carbon management and 

decarbonisation networks (Migo-Sumagang et al., 2022). Optimizing an enrolment process, the P-graph 

methodology identified structurally distinct workflow configurations that traditional approaches—often limited to 

local or sequential exploration—could not discover due to their partial search space coverage (Tapia et al., 

2022), with significant reductions in work time and resources. The five core elements of the P-graph model 

(inputs, outputs, operations, and their connections) enable sustainability-informed decisions. While Green BPM 

and LCA have supported sustainability in administrative processes, the integration of control strategies—such 

as Model Predictive Control (MPC) or nonlinear feedback—is still rare. Moreover, most P-graph applications 

focus on structural optimisation and lack support for dynamic decision-making. This study addresses this gap 

by combining control theory, P-graph modelling, and LCA to enable real-time, energy-aware administrative 

process optimisation. Focusing on university registration workflows, it explores how predictive, feedback-based, 

and nonlinear control strategies can improve energy efficiency, reduce CO₂ emissions, and enhance operational 

performance. LCA is used to assess the sustainability trade-offs between digital and paper-based workflows, 

offering a data-driven basis for environmentally conscious decisions. The study develops a control-theoretic 

framework for sustainable process management and extends the scope of P-graph methodology to include 

energy and emissions management in administrative services. It also introduces a control-integrated LCA 

approach to quantify CO₂ impacts across process types. Finally, it compares MPC, nonlinear, and hybrid 

optimisation strategies in terms of their effectiveness in supporting real-time, energy-efficient decisions in 

administrative systems. 

2. Materials and Methods 

The university admissions process was selected as the case study sample on the basis of its structured but 

dynamic nature, involving both digital and human-reliant administrative processes. The process involves some 

energy-hungry processes such as data capture, document processing, communication, approval processes, 

and handling of records. It further comprises both electronic infrastructure (e.g. databases, email) and non-

electronic processes (e.g. printing, filing, paper-based approvals), and thus is a natural candidate for assessing 

energy use-versus-CO₂-emissions trade-offs. Its cyclical demand—aligned with calendar cycles within 

academic calendars—also allows dynamic control-based optimization under varying workload conditions to be 

investigated. Initial review of the enrollment process revealed a series of inefficiencies in both energy usage and 

resource utilization. Some of these include the duplicate handling of documents (e.g. unnecessary data entry, 

416



multiple reviews), paper-based approval processes resulting in energy and material wastage, and unanticipated 

workload spikes, which result in inefficient use of IT infrastructure and human capital. 

To analyze and quantify such inefficiencies, the process was modeled using Business Process Modeling 

(BPMN) and P-graph techniques, then segmented into tasks according to their energy profile (energy intensity, 

digital or manual, fixed or variable load). The admission process was simulated as a dynamic system with inputs 

being student applications, human resources, and digital services, states being process stages such as 

registration, verification, and approval, and outputs being accepted/rejected applications and archived records. 

By state-space modeling, the dynamics of the process were mathematically characterized to assist in control 

strategy development. The P-graph framework was then used to determine optimal resource paths, with 

minimum energy and service quality preserved. P-graph was applied to define all feasible process structures, 

to examine resource dependencies and constraints, and to select lowest-cost, lowest-energy process options 

for varying input loads. This structural examination was then embedded in a broader control-based system, 

thereby enabling process paths to be dynamically realigned based on real-time feedback or predicted pattern 

of demand. 

To evaluate the dynamic sustainability performance of the process, three control strategies were examined: 

• Model Predictive Control (MPC): Used to predict future process loads based on past enrollment patterns, 

adjusting resource allocation and task scheduling accordingly. MPC allows anticipatory load balancing 

to avoid system overload and power spikes. 

• Nonlinear Control: Applied to capture complex interdependencies in the system, particularly when small 

variations in application volume cause disproportionate rises in resource utilization. The method 

enhances the process stability under variable, nonlinear conditions. 

• Hybrid Control (Discrete-Event + Continuous Control): Comprises discrete workflow leaps combined with 

real-time continuous power monitoring to make execution modes of tasks reconfigurable (manual/digital) 

at run time. 

All control strategies were compared against baseline (non-controlled) conditions using simulation-based 

analysis. 

3. Calculation 

To assess the environmental impact of the registration process in different configurations, a Life Cycle Analysis 

(LCA) was undertaken. This involved the estimation of energy consumption for each process alternative (fully 

digital, hybrid, paper), the CO₂ emission estimation from energy profiles for computing resources (computers, 

e-mail, storage) and physical resources (paper, print), and the aggregation of results into sustainability 

measures like carbon footprint per application processed, energy consumed per activity of the workflow, and 

process efficiency ratios. Scenario analysis was used to evaluate the effect of a full switch to a digital workflow, 

implementation of predictive scheduling with MPC, and utilization of adaptive reallocation of digital/human 

resources during periods of high load. The energy consumption of the university admissions process was 

approximated by defining process activities and assigning them typical energy consumption values based on 

literature and empirical estimates. All activities in the workflow were assigned as digital-intensive (email-based 

communication, database queries), manual-intensive (manual form processing, paper-based checking), or 

Mixed/conditional, depending on the specific configuration (staff document checking). Energy was measured in 

kWh per occurrence of an activity, supported by literature regarding ICT energy use and power consumption of 

common office equipment. CO₂ emissions were estimated from a regional electricity emission factor 

(kgCO₂/kWh) that can be used in the institutional context. For modeling the enrollment process's sustainability 

performance, the following scenarios were defined: every scenario was tested with varying numbers of students 

(1,000, 5,000, 10,000 applications) to verify scalability and nonlinear behavior under load. 

The formulas used for the calculations are for the energy consumption per task: 

𝐸𝑖 = 𝑃𝑖 ∗ 𝑡𝑖 ∗ 𝑛𝑖 (1) 

𝐸𝑡𝑜𝑡𝑎𝑙 =∑𝐸𝑖

𝑁

𝑖=1

 (2) 

Where Ei refers to the energy consumption of task i (in kWh), Pi denotes the power consumption of the device 

or activity used in task i (in kW), ti represents the time required to perform task i (in hours), ni indicates the 

number of instances of task i in the process, and N stands for the total number of tasks in the scenario process. 

The formula used for the calculations are for CO2 emission is: 

𝐶𝑂2𝑡𝑜𝑡𝑎𝑙 = γ ∗ 𝐸𝑡𝑜𝑡𝑎𝑙 (3) 

417



where: γ: Grid-specific emission factor (kg CO₂/kWh); Etotal: Total energy consumed by the process (kWh). 

The P-graph models were solved for each scenario to determine the optimal workflow structure, resource 

utilization efficiency, and energy-minimizing task sequences. Output metrics included minimum energy process 

pathways (kWh/application), redundancy elimination in task execution, and resource bottlenecks under control-

based reallocation. 

4. Results 

The total energy consumption for every 1,000 processed enrollment cases was calculated across five given 

scenarios. Outcomes indicated clear variation depending on workflow organization and the control strategy 

used. As shown in Table 1, the Hybrid Control strategy, combining discrete and continuous control approaches, 

demonstrated the lowest overall energy usage. This reflects its dynamic ability to alternate between manual and 

digital execution streams according to the dominating workload and energy cost, as modeled by the P-graph 

algorithm. For a regional average emission factor (γ=0.366 kg CO₂/kWh), CO₂ emissions and cost (0.20 

EUR/kWh on average) were calculated for all scenarios: 

Table 1: Energy consumption, CO₂ Emission Reduction and LCA Results 

Scenario Energy Use 

(kWh/1,000 cases) 

CO₂ Emissions 

(kg) 

Emission 

Reduction (%) 

Cost (EUR) 

Baseline 182.00 66.61 – 36.40 

Digital Static 128.42 47.00 29.4 25.70 

Digital + MPC 97.27 35.60 46.5 19.46 

Digital + Nonlinear 103.90 38.02 42.9 20.78 

Digital + Hybrid 89.58 32.78 50.8 17.92 

 

Figure 1 illustrates the total energy consumption and corresponding CO₂ emission reduction for five 

administrative workflow scenarios, demonstrating that control-based optimization, especially the Hybrid and 

MPC strategies, significantly lowers both energy demand and carbon footprint, with the Hybrid scenario 

achieving over 50 % reduction in both metrics. 

 

Figure 1: CO2 Emission and energy use comparison across five simulated control scenarios 

The hybrid model attained the highest overall performance with more than 50 % CO₂ emissions reduction and 

the lowest energy profile (89.58 kWh/1,000 applications). It adapted manual and digital tasks dynamically in real 

time, regulating the changes in workload in terms of resource usage. The MPC strategy attained notable savings 

(46.5 %) and was extremely effective for cyclic or anticipated workloads; however, it was rigid under uncertain 

demand. Nonlinear control was robust to uncertainty at the cost of slightly higher energy consumption and cost. 

These results confirm that optimization based on control significantly reduces the carbon footprint of 

administrative processes. Most importantly, MPC and hybrid control approaches show strong performance in 

high-load situations by their forecasting and adaptive features, respectively. The Hybrid Control scenario, 

derived through the P-graph-based structural enumeration, achieved the lowest energy consumption (89.58 

kWh) and highest CO₂ emission reduction (50.8 %). Compared to the baseline (182.00 kWh), this represents a 

50.7 % decrease in energy demand. This optimal configuration was discovered through P-graph's exhaustive 

search, which identified a workflow variant that bypasses redundant manual tasks under high load, an option 

not reachable via local or sequential optimization methods. Similarly, the MPC and Nonlinear scenarios, also 

generated from the P-graph solution space, showed 46.5 % and 42.9 % emission reductions respectively—

demonstrating how the model offers a suite of near-optimal alternatives depending on operational constraints 

and predictability of workloads. 

0.00

100.00

200.00

300.00

Baseline Digital
Static

Digital +
MPC

Digital +
Nonlinear

Digital +
HybridV
al

u
e 

(p
er

 1
,0

0
0

 
ap

p
lic

at
io

n
s)

Workflow Scenario

CO₂ Emissions (kg)

Energy Use (kWh/1,000 cases)

418



The P-graph framework models the process space using a bipartite graph structure composed of operating units 

(O-type nodes) and material/task elements (M-type nodes), with constraints embedded directly into the topology. 

In administrative workflows, tasks, resources, and conditions can be represented as nodes and arcs, allowing 

the exploration of all structurally feasible workflow variants. The generation of the maximal structure—which 

contains all potential process configurations—and the solution structure generation algorithm (SSG) enables full 

enumeration of all admissible workflow architectures. This approach ensures that the global optimum is 

identified, unlike conventional heuristics that often converge to local optima. P-graph analysis provided the 

following benefits to the control-guided decision-making process: Uncovered energy-optimal workflow routes in 

real-world limitations, revealed redundant task execution and inefficient routing in the baseline setting and 

enabled scenario-dependent guidance for re-arranged process flows (e.g. bypassing manual steps during surge 

demand). This embedding was made possible by integrating control parameters (e.g., time-varying demand, 

energy coefficients, and digital/manual task toggle rules) as boundary conditions within the combinatorial model, 

enabling structural adaptation in near real time. Though MPC is superior under structured environments with 

familiar patterns (e.g. semester enrollment increases), nonlinear control offers more resilience under chaotic 

changes in demand. However, the hybrid solution is the most equitable, combining prediction, flexibility, and 

structural optimization. Control-theoretic approaches offer practical sustainability gains in administrative 

workflows by enabling real-time resource distribution and flexible routing. The P-graph’s ability to explore all 

structurally feasible process variants supports informed, scenario-sensitive decision-making. Unlike traditional 

methods limited to a single solution, P-graph generates a maximal structure, ensuring global optimality. This 

was crucial in identifying the hybrid scenario, which achieved the best energy and emission outcomes—

demonstrating the method’s unique value for sustainability-focused digital transformation. 

5. Conclusions 

This study demonstrated how control theory principles, specifically Model Predictive Control (MPC), nonlinear 

control, and hybrid control approaches, could be effectively integrated with the P-graph approach to make 

administrative processes more sustainable and energy efficient. Using an example of a university registration 

workflow, we implemented five different process configurations ranging from manual to fully automated and 

dynamically optimized models. The integration with control-oriented optimization and scenario-dependent P-

graph modeling enabled the identification of energy-minimizing process paths, dynamic workload variation 

adaptation, and improved resource utilization. The results showed that control-theory-based approaches had 

remarkable saving in energy consumption and CO₂ emissions. Hybrid control model had optimal performance 

overall, with over 50 % of the reduction in emissions compared to baseline and higher capability to respond to 

changes in the process. Further, application of Life Cycle Assessment (LCA) provided crucial information on the 

environmental balance between manual and digital processes and justified the incorporation of sustainability 

indicators with operating models. While the simulation-based results are promising, the current study is not 

exempt from limitations. First, the energy consumption estimates are based on standardized task parameters 

and assumed mean infrastructure data, which may vary between institutions. Second, the control measures 

were implemented within a simulated environment rather than being introduced in an actual real-time system, 

limiting observations on operational details and user engagement. Third, current P-graph and control paradigms 

focus on structural optimality and short-term performance, ignoring multi-period decision-making and uncertainty 

of long-term demand projections. Increased research must be put towards developing a more detailed and 

dynamic control model that includes advanced workload prediction, cost-based constraints, and time-variable 

parameters. The results cumulatively confirm that the incorporation of control logic into administration processes 

yields significant sustainability benefits. Among all the models tested, hybrid control yielded the best overall 

results with least carbon footprint and energy usage, highest process flexibility and efficiency, and an extensible 

framework to various administration domains. Additional integration between the P-graph framework and 

mathematical control model will enable real-time process re-configuration and optimisation. Additional effort will 

also be put towards integrating control models into operational administrative systems, where real process data 

will be utilised to evaluate and verify model performance and refine energy and emissions estimates. Finally, 

future studies will attempt to broaden the analysis to more fields of public administration and incorporate 

extended LCA models to assess the long-run sustainability of control-based administrative modernization. 

Nomenclature

Ei - energy consumption of task i, kWh 

Etotal - total energy consumed by the process, kWh 

N - number of tasks in the scenario process 

ni - Number of instances of task i in the process 

Pi - power consumption of device or activity used in 

task iii, kW 

ti - time required for task i, h 

γ - grid-specific emission factor, kg CO₂/kWh 

419



References 

Bamdad K., Mohammadzadeh N., Cholette M., Perera S., 2023, Model predictive control for energy optimization 

of hvac systems using energyplus and aco algorithm. Buildings, 13(12), 3084. 

Brajer-Marczak R., Piwowar-Sulej K., 2023, Green Human Resource Management as an Element of Green 

Business Process Management. Humanities and Social Sciences, 30(3), 7-17. 

Cambaz N., Taskin E.G. Ruzgar A., 2018, Life cycle assessment of an office: Carbon footprint of an office staff. 

Environmental Research and Technology, 1(4), 34–39. 

Couckuyt D., Van Looy A., 2019, Green BPM as a business-oriented discipline: A systematic mapping study 

and research agenda. Sustainability, 11(11), 4200. 

Eisinger B., Buics L., 2024, Transforming public services management: A P-graph methodology case study and 

scenario analysis. Chemical Engineering Transactions, 114, 475-480. 

Friedler F., Aviso K.B., Bertok B., Foo D.C.Y., Tan R.R., 2019, Prospects and challenges for chemical process 

synthesis with P-graph. Current Opinion in Chemical Engineering, 26, 58–64. 

Fritsch A., von Hammerstein J., Schreiber C., Betz S., Oberweis A., 2022, Pathways to greener pastures: 

Research opportunities to integrate life cycle assessment and sustainable business process management 

based on a systematic tertiary literature review. Sustainability, 14(18), 11164. 

Gbededo M.A., Liyanage K., Garza-Reyes J.A., 2018, Towards a Life Cycle Sustainability Analysis: A 

systematic review of approaches to sustainable manufacturing. Journal of Cleaner Production, 184, 1002–

1015. 

Gohar S.R., Indulska M., 2020, Environmental Sustainability through Green Business Process Management. 

Australasian Journal of Information Systems, 24, 1–30. 

Liu L., Chen L., 2024, Investigating business process management on environmental performance considering 

the mediating role of information technology. Heliyon, 10(23), 39714. 

Magdaleno A.M., Duboc L., Betz S., 2017, How to incorporate sustainability into business process management 

lifecycle. Chapter In: Fahland D., Ghidini C., Becker J., Dumas M. (Eds.), Business Process Management 

Workshops: BPM 2016 International Workshops, Revised Papers 14, Springer International Publishing, 

Cham, Switzerland, 440–443. 

Mainini A.G., Pittau F., Casolari E., Simon Majour M., Cavaglià M., Riva R., Poli T., 2025, Enabling sufficiency 

through smart locks: Transforming office occupancy and building management for energy savings. Buildings, 

15(5), 669. 

Migo-Sumagang M.V., Tan R.R., Promentilla M.A.B., Aviso K.B., 2022, Applications of P-graph to carbon 

management: A mini-review. Chemical Engineering Transactions, 94, 31–36. 

Mirabella N., Rigamonti L., Scalbi S., 2013, Life cycle assessment of Information and Communication 

Technology application: A case study of dematerialization in the Italian Public Administration. Journal of 

Cleaner Production, 44, 115–122. 

Rathore A.S., Mishra S., Nikita, Priyanka P., 2021, Bioprocess control: Current progress and future perspectives. 

Life, 11(6), 557. 

Sohns T.M., Aysolmaz B., Figge L., Joshi A., 2023, Green business process management for business 

sustainability: A case study of manufacturing small and medium-sized enterprises (SMEs) from Germany. 

Journal of Cleaner Production, 401, 136667. 

Stadtländer M., Schoormann T., Knackstedt R., 2019, How software promotes the integration of sustainability 

in business process management, In: Ludwig T., Pipek V. (Eds), Proceedings der 14. Internationalen Tagung 

Wirtschaftsinformatik (WI 2019), Siegen, Germany, 1942–1953. 

Tapia J.F.D., Evangelista D.G., Aviso K.B., Tan R.R., 2022, P-graph attainable region technique (PART) for 

process synthesis. Chemical Engineering Transactions, 94, 1159–1164. 

Teplická K., Khouri S., Mehana I., Petrovská I., 2024, Energy Cost Reduction in the Administrative Building by 

the Implementation of Technical Innovations in Slovakia. Economies, 12(10), 260. 

Tick J., Tick A., 2013, Business process modeling—Simulation of administrative activities, 2013 IEEE 9th 

International Conference on Computational Cybernetics (ICCC), 345-348. 

Wesumperuma A., Ginige A., Ginige A., Hol A., 2013, Green Activity Based Management (ABM) for 

Organisations, In: Proceedings of the 24th Australasian Conference on Information Systems (ACIS 2013), 

Paper 144. 

Yi J., 2025, Digital government and carbon emissions: Evidence from China. Frontiers in Environmental 

Science, 13, 1470724. 

Yousufi M.K., 2023, Exploring paperless working: A step towards low carbon footprint. European Journal of 

Sustainable Development Research, 7(4), 1-8. 

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	Sustainability and Energy Efficiency in Administrative Processes: A Control Theory Approach with P-graph Optimization