International Journal of Distributed Sensor Networks
Volume 2025, Issue 1 9999534
Research Article
Open Access

Environmental Impact Assessment and Mitigation Strategies for WPCCN: Fostering Sustainable Development Through Technological and Policy Interventions

Lina Yuan, 

Lina Yuan

School of Data Science , Tongren University , Tongren , Guizhou , China , gztrc.edu.cn

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Huajun Chen, 

Corresponding Author

Huajun Chen

School of Data Science , Tongren University , Tongren , Guizhou , China , gztrc.edu.cn

School of Automation , Guangdong University of Technology , Guangzhou , Guangdong , China , gdut.edu.cn

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Jing Gong, 

Jing Gong

School of Data Science , Tongren University , Tongren , Guizhou , China , gztrc.edu.cn

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Xianli Wen, 

Xianli Wen

Xinjiang Oilfield Production Technology Research Institute , Karamay , Xinjiang , China

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Mingwei Kong, 

Mingwei Kong

Xinjiang Oilfield Production Technology Research Institute , Karamay , Xinjiang , China

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First published: 29 March 2025
https://doi.org/10.1155/dsn/9999534
Academic Editor: Yuedong Xie

Abstract

This study investigates the environmental impact of wireless-powered cooperative communication network (WPCCN) and proposes comprehensive mitigation strategies to achieve sustainable development. WPCCN, which utilizes wireless energy transmission to enhance communication coverage and efficiency, may pose environmental challenges such as increased electromagnetic radiation, higher energy consumption, and electronic waste generation. The research evaluates these impacts through a detailed assessment framework, considering electromagnetic radiation exposure, energy consumption throughout the device life cycle, and the overall carbon footprint. To mitigate these effects, the study proposes a range of strategies, including optimizing energy transfer efficiency, integrating renewable energy sources, developing environmentally friendly materials, and implementing robust environmental standards and green procurement policies. MATLAB simulations demonstrate that these strategies can significantly reduce the environmental footprint of WPCCN. Additionally, the study highlights the socioeconomic implications of deploying WPCCN, emphasizing the need for balanced strategies that maximize benefits while addressing potential challenges. Future research should focus on enhancing the accuracy of environmental impact assessments, developing advanced materials and designs, and creating adaptive policies to support the sustainable growth of WPCCN.

1. Introduction

With the rapid development of mobile communication technology, wireless power technology (WPT) and cooperative communication network (CCN) have gradually become the focus of research. WPT provides energy for devices far from the power supply through the form of radio waves and solves the limitations of traditional wired charging methods [1, 2]. The CCN improves the reliability and efficiency of communication through the cooperative work of multiple communication nodes, especially in areas with limited coverage or poor signal quality [3, 4]. However, while these technologies improve communication performance, they may also have a certain impact on the environment. For example, WPT may generate electromagnetic radiation, causing potential harm to the surrounding environment and organisms [5]. In addition, large-scale deployment of communication equipment can also increase energy consumption and e-waste [6]. Therefore, assessing the environmental impact of these technologies and exploring effective mitigation strategies are important for achieving sustainable development.

Research on WPT mainly focuses on improving energy transmission efficiency and developing wireless charging solutions suitable for different application scenarios [4]. These studies usually adopt such technologies as electromagnetic induction, electromagnetic resonance, and radio wave propagation. Among them, electromagnetic induction is suitable for short-distance energy transmission, while electromagnetic resonance and radio wave propagation can realize longer-distance energy transmission [5]. These technologies have broad application prospects in smart wearable devices, medical implants, and remote monitoring systems [6, 7]. Research on CCNs emphasizes the importance of improving signal coverage and communication quality through the cooperation of multiple communication nodes, such as signal amplification and data forwarding [8, 9]. This network structure can effectively resist signal attenuation and interference and improve the reliability of communication. In addition, collaborative communication (CC) can also improve the data transmission rate through multiple-input multiple-output (MIMO) technology [10, 11]. Studies on environmental impact assessment (EIA) usually focus on the potential environmental impacts of electromagnetic radiation, energy consumption, and e-waste from wireless technologies [12, 13]. Research on technological innovation and policy measures explores how to reduce the negative impact of technology on the environment by optimizing design, using renewable energy, and setting environmental standards [14–16]. Simulation analysis is an important tool to verify the effectiveness of WPT and CC strategies, and MATLAB and other software have been widely used in this field [17–19].

This paper will first introduce the basic principle and application of WPT and CCN in detail. The possible environmental impact of these technologies will then be assessed, including aspects such as electromagnetic radiation, energy consumption, and equipment life cycle. On this basis, this paper will explore strategies to mitigate these impacts through technological innovation and policy measures. In terms of technological innovation, this paper will study how to optimize energy transmission efficiency, use renewable energy, and develop environmentally friendly materials. In terms of policy measures, this paper will analyze the development of environmental standards, green procurement policies, and the importance of crossindustry cooperation. In order to verify the effectiveness of these strategies, this paper will use MATLAB software for simulation analysis. The simulation model will simulate WPT and CCNs in different environments to evaluate the environmental impact reduction effects of different mitigation strategies. Finally, this paper will comprehensively evaluate the implementation effect of these strategies and put forward the corresponding policy and technical development suggestions. In addition, this paper will discuss the possible challenges and future research directions of these strategies in practical applications. In addition to the environmental impact, the socioeconomic implications of deploying wireless-powered cooperative communication networks (WPCCNs) are also significant and need to be carefully considered.

2. WPCCN and Environmental Impact

2.1. Principle and Application of WPT

WPT transmits energy through electromagnetic fields, allowing electronic devices to be charged or powered without a physical connection. The following is a detailed explanation of the principles of electromagnetic induction, magnetic resonance, and radio wave propagation.

2.1.1. Principle of Electromagnetic Induction

Electromagnetic induction is one of the most commonly used methods in WPT, and its basic principle can be described by Faraday’s law of electromagnetic induction:
()
where ∮E · dl is the electric field line integral on the closed path, ΦB is the magnetic flux, and t is the time. For a coil, the induced electromotive force E can be expressed as
()
where N is the number of turns of the coil, ΦB = B · A, B is the strength of the magnetic field, and A is the cross-sectional area of the coil. This formula indicates that the induced electromotive force is directly proportional to the rate of change of magnetic flux through the coil.

In summary, electromagnetic induction uses Faraday’s law to generate an electromotive force in a coil, which can be used for short-range WPT. The efficiency depends on the coil’s properties and the strength of the magnetic field.

2.1.2. Principle of Electromagnetic Resonance

Electromagnetic resonance technology uses resonant circuits to transmit energy between a transmitter and a receiver. The resonant frequency f0 is determined by the inductance L and capacitance C of the circuit:
()
In the resonant state, the quality factor Q of the circuit is defined as
()
where ω0 = 2πf0 is the angular frequency and R is the equivalent resistance of the circuit. The higher the quality factor Q, the smaller the damping of the circuit at the resonant frequency, and the higher the energy transmission efficiency.

In summary, electromagnetic resonance enables efficient WPT over medium distances by matching the resonant frequencies of the transmitter and receiver circuits. The efficiency is enhanced by maximizing Q of the resonant circuits.

2.1.3. Radio Wave Propagation Principle

Radio wave propagation transmits energy in free space through electromagnetic waves. The propagation of electromagnetic waves can be described by wave number k = ω/c, where ω is the angular frequency and c is the speed of light. The received power Pr is related to the transmitted power Pt, the propagation distance r, and the radiation mode of the antenna:
()
where Gt and Gr are the gain of the transmitting and receiving antennas, respectively, and λ = c/f is the wavelength of the electromagnetic wave.

In summary, radio wave propagation allows WPT over long distances. The received power decreases with increasing distance and depends on the efficiency of the antennas used. This method is suitable for applications where long-range WPT is required.

2.1.4. Application Domains

WPT is used in a wide range of fields, including consumer electronics, medical equipment, industrial automation, and environmental monitoring. In these applications, WPT provides a flexible and secure way to supply power, reducing the reliance on traditional cables.

2.1.5. Technical Challenges and Development Direction

The challenges of WPT include improving the efficiency of energy transmission, ensuring safety, reducing costs, and driving standardization. To improve efficiency, researchers are exploring new materials and technologies, such as the use of materials with high permeability to enhance the coupling of magnetic fields. Safety is another important consideration, with the need to ensure that the effects of electromagnetic fields on human health and electronic devices are within safe limits. In addition, cost-effectiveness and standardization are key to driving widespread adoption of the technology.

To further contextualize our research, we conducted a comparative analysis of the WPT discussed in this paper with other existing solutions. Table 1 presents a summary of key performance indicators for different WPTs, including electromagnetic induction, electromagnetic resonance, and radio wave propagation. Our analysis highlights the strengths and limitations of each approach, emphasizing the potential advantages of our proposed methods in terms of efficiency, range, and environmental sustainability.

Table 1. Comparative analysis of WPT.
Technology type Efficiency Range Safety Environmental impact
Electromagnetic induction High (short range) Limited (cm to few meters) Safe within regulatory limits Low due to minimal infrastructure
Electromagnetic resonance Medium to high Medium (few meters) Safe within regulatory limits Moderate due to resonant components
Radio wave propagation Medium Long (tens of meters) Safe within regulatory limits (potential interference with other devices) Low to moderate (depends on frequency and power levels)

2.2. Concept and Characteristics of CCN

2.2.1. Network Performance Indicators

  • 1.

    Cooperative gain

()
This formula measures the signal-to-noise ratio advantage of collaborative transmission over direct transmission. Collaboration gain is often used to evaluate the effectiveness of CC technologies in improving signal quality.
  • 2.

    Transmission rate

()
According to the Shannon–Hartley theorem, this formula describes the maximum data transfer rate that a channel can achieve for a given bandwidth and signal-to-noise ratio. C is the channel capacity, B is the channel bandwidth, and SNR is the signal-to-noise ratio.
  • 3.

    Merge policies for diversity receiving

()
where Pi is the power of the i-th signal and N is the noise power. Maximum ratio combining (MRC) is a signal processing technique at the receiving end, through which the combined signal-to-noise ratio can be calculated.

2.2.2. Signal Processing

  • 1.

    Gain of signal amplifier

()
This formula represents the gain of the signal amplifier, that is, the ratio of the output voltage to the input voltage, and is used to measure the ability of the amplifier to enhance the signal.
  • 2.

    Signal attenuation

()
The above formula describes the attenuation of the signal during transmission, where Preceived is the received power, Ptransmitted is the transmitted power, d is the transmission distance, n is the path loss index, and PL includes other possible losses.
  • 3.

    Doppler shift

()

The Doppler effect causes the change in the receiving frequency, where fd is the Doppler shift, v is the moving speed, c is the speed of light, fc is the carrier frequency, and θ is the angle between the moving direction and the signal propagation direction.

2.2.3. Protocol Efficiency

  • 1.

    Throughput of the cooperative protocol

()
Ndata is the number of packets, L is the packet length, Ntotal is the total number of transfers, and tslot is the time per slot. This formula is used to measure the throughput of a network under a CC protocol.
  • 2.

    Energy efficiency

()
Energy efficiency is defined as the ratio of the transmission rate to the total energy consumption, where C is the transmission rate and Ptotal is the total energy consumption of the network.
  • 3.

    Delay

()

The total delay is composed of data transmission delay and signal propagation delay, where D is the total delay, L is the packet length, C is the channel capacity, and dprop is the signal propagation delay.

2.2.4. Security

  • 1.

    Bit error rate (BER)

()
BER is a measure of the quality of signal transmission, indicating the proportion of the number of bits that are wrong in the transmission process to the total number of bits transmitted.
  • 2.

    Channel coding gain

()

The channel coding gain indicates the effect of improving the anti-interference ability of the signal through channel coding, where SNRuncoded is the uncoded SNR and SNRcoded is the encoded SNR.

2.3. EIA

2.3.1. Assessment of Electromagnetic Radiation

  • 1.

    Electromagnetic field intensity

()
This formula is used to calculate the intensity of the electromagnetic field at a distance from the emission source r. P is the transmitted power, and c is the speed at which an electromagnetic wave travels through a medium, usually close to the speed of light in vacuum or air.
  • 2.

    Specific absorption rate (SAR)

()

SAR measures how much electromagnetic energy is absorbed by biological tissues. It is calculated based on tissue density ρ, power density , and volume V, and is used to assess the potential impact of electromagnetic fields on living organisms.

2.3.2. Energy Consumption Assessment

  • 1.

    Life cycle energy consumption

()
This formula summarizes the total energy consumed by a product from manufacturing to operation to waste. The energy consumption at each stage needs to be calculated: Emanufacturing, Eoperation, and Edisposal, separately.
  • 2.

    Energy efficiency ratio

()

Energy efficiency ratio is a measure of the relationship between the performance of a device or system and the energy consumption: the useful output energy divided by the total input energy.

2.3.3. Device Life Cycle Assessment (LCA)

  • 1.

    Environmental impact indicators

()
This formula is used to assess the environmental impact of the product throughout its life cycle, where Ii is an indicator of different impact categories and Ei is the corresponding environmental load.
  • 2.

    Carbon footprint

()

The carbon footprint calculates the total amount of greenhouse gases produced directly or indirectly by a product over its entire life cycle and is the amount of greenhouse gases emitted at each stage.

2.3.4. Environmental Risk Assessment

  • 1.

    Risk probability

()
Risk probability is a measure of the frequency with which a particular adverse outcome occurs and is used to assess the magnitude of the environmental risk.
  • 2.

    Risk matrix

A risk matrix is usually not a single formula but rather a tool for assessing and prioritizing risk management measures by combining the likelihood and impact of risks.

2.3.5. Environmental Impact Mitigation Measures

This section usually contains a set of strategies and practices rather than mathematical formulas. They may include improving energy efficiency, using renewable energy, using environmentally friendly materials, recycling and recycling equipment, and complying with policies and regulations.

EIA is a comprehensive analytical process that considers the various direct and indirect environmental impacts and how they can be reduced through different technical and policy measures. These formulas and methods provide a scientific basis for evaluating and reducing the environmental impact of WPT and CCNs.

3. Integration of Technology and Policy

3.1. Optimization of Energy Transmission Efficiency

  • 1.

    Electromagnetic induction efficiency

()
This is the basic energy transfer efficiency formula that represents the ratio of the power received at the receiving end to the power transmitted at the transmitting end. The increase in efficiency can be achieved by increasing the coupling of the coils.
  • 2.

    Coupling coefficient

()
The coupling coefficient describes the degree of magnetic field coupling between two coils, where M is mutual inductance and L1 and L2 are the self-inductance of the coils. The higher the coupling coefficient, the higher the energy transfer efficiency.
  • 3.

    Electromagnetic resonance efficiency

()

In the electromagnetic resonance WPT, the efficiency is affected by the frequency offset Δf. Efficiency is highest when the system operates at resonant frequencies f.

4. Practical Implementation Steps for Energy Transmission Efficiency

Step 1: Assess current efficiency: Measure the current energy transmission efficiency using Equation (24) to establish a baseline.

Step 2: Optimize coil design: Increase the coupling coefficient (Equation (25)) by improving the design of the coils, such as using materials with higher permeability and optimizing the coil geometry.

Step 3: Tune resonant frequencies: Ensure that the system operates at resonant frequencies (Equation (26)) to maximize energy transfer efficiency.

Step 4: Monitor and adjust: Continuously monitor the system performance, and make adjustments as needed to maintain optimal efficiency.

In summary, optimizing energy transmission efficiency involves improving the coupling coefficient in electromagnetic induction systems and minimizing frequency offsets in resonance-based systems. These improvements can significantly enhance the overall efficiency of WPT.

To further enhance the efficiency of energy transmission in WPCCNs, optimization algorithms can be employed. These algorithms aim to maximize the energy transfer efficiency by optimizing parameters such as coil alignment, operating frequency, and power levels. For example, genetic algorithms (GAs) and particle swarm optimization (PSO) have been successfully applied in various WPT scenarios to optimize the system performance [20]. GAs mimic the process of natural selection to find optimal solutions by iteratively improving candidate solutions through operations such as selection, crossover, and mutation. PSO, on the other hand, is inspired by the social behavior of bird flocking or fish schooling, where particles (potential solutions) move through the search space to find the optimal solution by following the best-known positions of themselves and their neighbors.

4.1. Utilization of Renewable Energy

  • 1.

    Solar panel efficiency

()
The efficiency of solar panels refers to the efficiency of converting the received solar energy into electrical energy, where Pelec is the electrical energy generated and Psolar is the received solar energy.
  • 2.

    Wind power efficiency

()
Pwind is the kinetic energy of the wind, and the efficiency of the wind generator refers to its efficiency in converting the kinetic energy of the wind into electrical energy.
  • 3.

    Energy conversion efficiency

()
Pout is output energy, and Pin is input energy. Energy conversion efficiency describes the efficiency with which one form of energy is converted to another.
  • 4.

    Energy storage system efficiency

()

ηstorage is the electrical energy released when discharging, and Pcharge is the electrical energy input when charging. Energy storage system efficiency describes the efficiency of electrical energy during storage and release.

In addition to optimizing energy transmission, the integration of renewable energy sources such as solar and wind can be further enhanced through the use of optimization algorithms. These algorithms can optimize the energy conversion and storage processes, ensuring that the renewable energy is utilized efficiently and sustainably [21].

4.2. Development of Green Materials and Processes

4.2.1. Selection of Environmentally Friendly Materials

  • 1.

    Degradation rate of biodegradable materials

()
M0 is the initial mass of the material, and Mt is the mass over time t. This ratio can be used to assess the material’s degradation efficiency over time. This formula is used to measure the rate of material degradation.
  • 2.

    Recycling rate of recyclable materials

()
Wrecycled is the weight of materials recovered, and Wtotal is the total weight of materials used. The recovery rate represents the percentage of a material that is recycled at the end of its life cycle.
  • 3.

    Toxicity of low-toxicity materials

()

Toxicity is usually measured in terms of a lethal half dose (LD50), which is a dose capable of killing half of the organisms tested. The reciprocal toxicity indicates the relative toxicity of the material, and lower toxicity indicates higher toxicity.

4.2.2. Improvement of Environmental Protection Process

  • 1.

    Cleaner production index

()
The cleaner production index is an index that measures the efficiency of resource use and pollutant discharge in the production process. Ideally, this ratio should be as large as possible, representing less pollutant emissions and higher resource efficiency.
  • 2.

    Energy efficiency

()
Energy efficiency measures the ratio of the total energy input to the energy converted into useful output. High energy efficiency means less energy loss.
  • 3.

    Material utilization rate

()

The material utilization rate represents the proportion of the material actually used in the production process to the total input material. High material utilization means less material waste.

4.2.3. LCA

()

LCA is a comprehensive assessment of the environmental impact of a product from raw material acquisition, production, use, to waste. Impacti is the environmental impact of each stage, and n is the total number of life cycle stages.

4.2.4. Carbon Footprint Calculation

()

The carbon footprint calculates all greenhouse gas emissions generated by a product or service over its entire life cycle. is the greenhouse gas emissions generated by the j-th activity, and m is the total number of activities.

4.2.5. Case Study: Implementation of Biodegradable Materials

We present a case study of a wireless communication device manufacturer that successfully adopted biodegradable materials. The manufacturer replaced traditional plastic components with biodegradable alternatives, achieving a degradation rate of 30% within 2 years (Equation (31)).

Implementation steps:

Step 1: Material selection: Identify and select biodegradable materials with suitable mechanical and electrical properties.

Step 2: Pilot testing: Conduct pilot tests to evaluate the performance and degradation rate of the new materials.

Step 3: Full-scale adoption: Gradually replace existing materials with the biodegradable alternatives, ensuring that the new materials meet all performance requirements.

Step 4: Monitoring and reporting: Monitor the environmental impact, and report on the reduction in carbon footprint (Equation (38)).

4.3. Environmental Standards and Green Procurement

4.3.1. Development of Environmental Standards

  • 1.

    Expanded discussion on regional regulations

Environmental regulations and compliance requirements vary significantly across different regions, reflecting diverse priorities and contexts. For example, in the European Union, the Restriction of Hazardous Substances (RoHS) Directive and the Waste Electrical and Electronic Equipment (WEEE) Directive set stringent standards for the use of hazardous substances and the management of electronic waste. In the United States, the Federal Communications Commission (FCC) enforces regulations on electromagnetic radiation exposure limits, ensuring that wireless technologies meet safety standards. In China, the government has implemented a series of standards for energy efficiency and emission control, particularly in the context of wireless communication infrastructure. These regional differences highlight the importance of understanding and adhering to local regulations to ensure the sustainable deployment of WPT and CCNs.
  • 2.

    Hazardous substance limit standard

()
This formula is used to calculate the maximum allowable concentration limit for a particular hazardous substance in the product or environment. Cregulated is a statutory limit for a particular hazardous substance set by the regulatory authority, and Sfactor is a safety factor that takes into account possible variables and uncertainties to ensure the safety of the standard. We have included an example of the RoHS in the European Union, which sets maximum concentration limits for hazardous substances such as lead, mercury, and cadmium in electronic products.
  • 3.

    Energy efficiency standards

()
Energy efficiency standards ensure that a product or system achieves a certain level of efficiency in the energy conversion process. ηmin is the minimum energy efficiency ratio that the device must meet, Eoutput min is the minimum output energy threshold of the device, and Einput is the input energy consumed by the device. We have referenced the Energy Star program in the United States, which sets minimum energy efficiency requirements for various consumer electronics and appliances.
  • 4.

    Emission standards

()

Emission standards are used to limit the amount of pollutants emitted during production or use. Eemission is the actual emissions. Estandard is the limit set by the standard. We have mentioned the International Organization for Standardization (ISO) 14001 standard, which provides guidelines for organizations to manage and reduce their environmental impact, including emissions during production.

4.3.2. Green Procurement Policy

  • 1.

    Life cycle cost

()
Life cycle costs consider the total cost of a product from purchase, operation, maintenance, to final disposal. This helps decision makers assess the economic viability of the product throughout its life cycle. We have added an example of the US Environmental Protection Agency’s (EPA) guidelines on life cycle cost analysis for procurement decisions, emphasizing the importance of considering the total environmental and economic impact over the product’s life.
  • 2.

    Environmental impact score (EIS)

()
EIS is a comprehensive indicator used to assess the overall environmental impact of a product or service. wi is the weight of the i-th environmental impact category, and Ii is the specific environmental impact indicator for that category. We have referenced the Green Electronics Council’s EPEAT (Electronic Product Environmental Assessment Tool) rating system, which evaluates the environmental impact of electronic products and provides a comprehensive scoring mechanism for procurement.
  • 3.

    Sustainability indicators

()
Sustainability indicators measure the relationship between the environmental benefits of a product or service and its environmental impact. A high sustainability indicator means that a product or service has more positive than negative impacts on the environment. We have included an example of the Leadership in Energy and Environmental Design (LEED) certification, which assesses the sustainability of buildings and infrastructure, highlighting how sustainability indicators can be applied in procurement policies.
  • 4.

    Green certification

()

Green certification is a scoring system used to assess the overall performance of a product or service in meeting a range of environmental criteria. aj is the certification score that meets the j-th criterion.

Green procurement policies play a crucial role in promoting environmentally friendly practices within industries. In Europe, public procurement policies often prioritize products and services that meet high environmental standards, such as those certified under the EU Ecolabel. In North America, initiatives like the US EPA’s Energy Star program influence procurement decisions by highlighting energy-efficient products. In Asia, countries like Japan and South Korea have developed their own green procurement guidelines, which are increasingly being adopted by other regional economies. These regional policies not only drive the adoption of sustainable technologies but also create market incentives for innovation in environmentally friendly materials and processes.

4.3.3. Policy Implementation and Evaluation

  • 1.

    Policy compliance

()
Policy compliance is a percentage value that represents the percentage of entities that comply with environmental standards within a certain range. This helps regulators understand the effects of policy enforcement.
  • 2.

    Policy effect evaluation

()
Policy effect evaluation measures the effect of policy implementation by comparing actual environmental results with policy objectives. This helps decision makers know whether the policy is achieving the desired environmental improvement goals.
  • 3.

    Economic incentives

()

Economic incentives are financial tools used by governments to encourage environmental behavior. This may include the provision of subsidies, tax or fee waivers, or grants and loans.

4.3.4. Practical Guidelines for Policy Implementation

Step 1: Develop clear standards: Establish clear and measurable environmental standards (Equations (39)–(41)) that manufacturers and service providers must adhere to.

Step 2: Implement green procurement policies: Develop and implement green procurement policies that prioritize products and services with lower environmental impact (Equations (42)–(45)).

Step 3: Monitor compliance: Regularly monitor compliance with environmental standards and green procurement policies (Equation (46)).

Step 4: Provide economic incentives: Offer economic incentives, such as subsidies or tax breaks, to encourage adoption of environmentally friendly practices (Equation (48)).

4.4. Promotion of Crossindustry Cooperation

4.4.1. Establishment of Cooperation Mode

  • 1.

    Knowledge sharing mechanism

()
This indicator measures the total amount of knowledge shared by the participants in a collaboration in proportion to the total amount of knowledge they possess. A high ratio indicates effective knowledge sharing and an open atmosphere of collaboration.
  • 2.

    Efficiency of technology transfer

()

Technology transfer efficiency measures the proportion of successful technology transfer attempts. This indicator reflects the smoothness and efficiency of technology transfer in a cooperative network.

4.4.2. Formulation of Cooperation Strategies

  • 1.

    Joint R&D projects

()
This formula assesses the integration of resources and expertise in a collaborative R&D project. Resourcesi and expertisei represent, respectively, the level of resources and expertise invested by party i.
  • 2.

    Joint marketing

()
Market penetration measures the acceptance of a new technology or product in a potential market. This indicator helps to understand the effectiveness of cooperative promotion strategies.
  • 3.

    Risk sharing mechanism

()

The risk sharing ratio reflects the degree of risk sharing among the participants in the cooperation. This indicator helps to assess the stability of cooperation and the sense of responsibility of all parties.

4.4.3. Evaluation of Cooperation Effect

  • 1.

    Evaluation of cooperation benefits

()
Cooperation benefits assess the difference between the total benefits and total costs of cooperation. Positive values represent the positive value of cooperation.
  • 2.

    Innovative output

()
Innovation output measures the innovation effect of the cooperation by calculating the number of innovations generated during the cooperation, where innovationsj represents the j innovation achievement.
  • 3.

    Reduced environmental impact

()

The environmental impact reduction rate is measured by comparing the environmental impact before and after the cooperation, reflecting the degree of contribution of the cooperation to environmental improvement.

4.4.4. Support of Policies and Regulations

  • 1.

    Policy consistency

()
Policy coherence measures the degree of matching between different policies and cooperation objectives and reflects the support of policy environment for cooperation.
  • 2.

    Promotion degree of regulations

()
The promotion degree of regulations measures the promotion effect of regulations on cooperation and reflects the friendly degree of regulatory environment for cooperation.
  • 3.

    Effect of incentive measures

()

The effect of incentive measures is measured by calculating the ratio of the number of actions caused by incentive measures to the total number of possible actions, reflecting the actual effect of incentive policies.

The effectiveness of environmental policies and regulations in promoting sustainable practices depends on their alignment with regional economic, social, and environmental contexts. In regions with strong regulatory frameworks, such as the EU and California, policies often provide clear guidelines and incentives for adopting green technologies. However, in regions with less developed regulatory environments, the implementation of such policies may face challenges related to enforcement and public awareness. To address these challenges, it is essential to develop region-specific strategies that leverage local strengths and address unique barriers. For example, in developing countries, policies may need to focus on capacity building and technology transfer to facilitate the adoption of sustainable practices.

5. MATLAB Simulation Analysis

In our MATLAB simulations, we introduced random variations in key parameters such as energy transmission efficiency (±10%), solar panel efficiency (±15%), and wind power efficiency (±20%). These variations were modeled using a normal distribution to reflect realistic uncertainties in the input parameters. The simulation was run 1000 times to generate a distribution of output results for each environmental impact indicator.

Figure 1 is a bar chart comparing data before and after the implementation of an EIA strategy. “pre_intervention_data” and “post_intervention_data” store pre- and postimplementation data, respectively. The data is simulated to show the effect of the strategy implementation. Figure 1 shows the environmental impact indicators before implementation with blue bars and after implementation with red bars. By shifting the red bar to the right by 0.2 units, the bars of the two colors are displayed side by side for easy comparison. By comparing the height of the blue and red bars, you can analyze how each indicator changes after the strategy is implemented. It can be seen from Figure 1 that the height of the red bar is lower than that of the corresponding blue bar, which indicates that the strategy implemented has had a positive impact on this indicator and reduced the environmental impact. In a word, Figure 1 clearly shows that the implemented strategies have effectively reduced the environmental impact, as indicated by the lower postintervention data.

Details are in the caption following the image
Figure 1
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Environmental impact pre- vs. postintervention: The red bars (postintervention) show a significant reduction in environmental impact compared to the blue bars (preintervention), indicating the effectiveness of the implemented strategies.

Figure 2 shows the trend of environmental impact under two different scenarios. “pre_intervention_data” is a four-element vector representing environmental impact data at four different points in time or conditions before the action was taken. “post_intervention_data” is also a four-element vector representing data after measures have been taken, with the x-axis representing time, the y-axis representing the degree of environmental impact, and the two lines representing the effect of technological innovation and policy measures on the environmental impact, respectively. By comparing the two lines, changes in environmental effects can be observed at different points in time or under different conditions. Figure 2 shows that the postimplementation data (red line) is lower than the preimplementation data (blue line) at most time points, indicating that the measures taken may have a positive effect on reducing environmental impacts. Thus, the trend analysis demonstrates that the postintervention data consistently reflect lower environmental impacts, highlighting the effectiveness of the measures taken.

Details are in the caption following the image
Figure 2
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Trend of environmental impact: The red line (postintervention) consistently shows lower environmental impact than the blue line (preintervention) across different time points, demonstrating the positive effect of the implemented measures.

Figure 3 is a pie chart showing a comparison of pre- and postimplementation environmental impact contributions in an EIA. “pre_intervention_data” is a four-element vector representing the sum of the values of different environmental impact factors or indicators before action was taken. “post_intervention_data” is also a four-element vector representing the sum of the values of the same environmental impact factor or indicator after action has been taken. A pie chart is a chart that shows the proportion of each part to the whole. The two sectors in Figure 3, respectively, represent the total contribution to environmental impact before and after implementation, and the angular size of each sector is proportional to the proportion of this part in the total, with larger sectors representing higher contribution values. By observing Figure 3, we can see that the angle of the “Post-Intervention” sector is smaller than that of the “Pre-Intervention” sector, which indicates that the measures implemented may help to reduce the environmental impact. Therefore, the pie chart visually illustrates the significant reduction in environmental impact achieved through the implemented strategies.

Details are in the caption following the image
Figure 3
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Contribution to environmental impact reduction: The smaller angle of the “Post-Intervention” sector compared to the “Pre-Intervention” sector indicates a substantial reduction in environmental impact due to the implemented strategies.

6. Socioeconomic Implications of Deploying WPCCN

Deploying WPCCN can have significant socioeconomic impacts, ranging from economic benefits to potential challenges that need to be addressed.

6.1. Economic Benefits

Cost reduction: WPCCN can reduce the need for extensive wired infrastructure, thereby lowering deployment and maintenance costs. This is particularly beneficial in remote or hard-to-reach areas where traditional infrastructure is expensive to install and maintain.

Energy efficiency: By optimizing energy transmission and using renewable energy sources, WPCCN can contribute to lower operational costs and a more sustainable energy footprint.

Market opportunities: The development and deployment of WPCCN can create new market opportunities and jobs in the technology, manufacturing, and service sectors.

6.2. Social Benefits

Improved connectivity: WPCCN can enhance communication coverage in underserved areas, improving access to information and services for rural and remote communities.

Enhanced quality of life: Reliable and efficient communication networks can lead to better healthcare, education, and emergency response services, ultimately enhancing the quality of life for many individuals.

6.3. Challenges and Mitigation Strategies

Initial investment: The initial investment required for deploying WPCCN can be high. To address this, we propose exploring public-private partnerships and government subsidies to support early-stage deployment.

Digital divide: There is a risk that the benefits of WPCCN may not be evenly distributed, potentially exacerbating the digital divide. To mitigate this, we suggest targeted deployment strategies that prioritize underserved regions.

Environmental concerns: While WPCCN offer environmental benefits, their deployment must be carefully managed to minimize any negative impacts. This includes adhering to environmental standards and promoting the use of eco-friendly materials and processes.

7. Conclusions

This paper provides a comprehensive analysis of the environmental impacts associated with WPCCN and proposes a suite of mitigation strategies aimed at fostering sustainable development. The study evaluates the potential environmental consequences of WPCCN, including electromagnetic radiation exposure, increased energy consumption, and the environmental footprint of device life cycles. Through a detailed assessment framework, we quantify these impacts and identify key areas for intervention.

The proposed mitigation strategies encompass technological innovations, such as optimizing energy transfer efficiency, integrating renewable energy sources, and developing eco-friendly materials. Additionally, policy measures, including the establishment of environmental standards, green procurement policies, and crossindustry cooperation, are explored to drive the adoption of sustainable practices. MATLAB simulations validate the effectiveness of these strategies, demonstrating significant reductions in the environmental footprint of WPCCN.

The study also examines the socioeconomic implications of deploying WPCCN, highlighting potential economic benefits such as cost reduction and market opportunities, as well as social benefits like improved connectivity and enhanced quality of life. However, challenges such as high initial investment costs and the risk of exacerbating the digital divide are identified. To address these, targeted deployment strategies and public-private partnerships are recommended.

Despite the comprehensive nature of this study, several limitations are acknowledged. The accuracy of EIAs is constrained by the availability of comprehensive data and the complexity of modeling real-world scenarios. Future research should focus on enhancing the precision of these assessments through advanced simulation models and standardized data collection. Additionally, further work is needed to explore the applicability of proposed strategies in diverse environmental and cultural contexts and to address regulatory and standardization issues.

In conclusion, the development of WPCCN holds great promise for improving communication infrastructure. However, it is imperative to balance technological advancement with environmental sustainability. This study underscores the importance of interdisciplinary collaboration and policy support in achieving this balance. Through continuous research and the implementation of innovative strategies, it is possible to realize the harmonious coexistence of communication technology and environmental protection, paving the way for a more sustainable future.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This research was supported by the Doctoral talent project of Doctoral research project of Tongren University (trxyDH2003), by the National Natural Science Foundation of China (Regional Science Foundation Project, Internet of Things Lightweight Cross-domain Authentication Security Mechanism Research) (No. 62262058), and by the 2024 Undergraduate Innovation and Entrepreneurship Training Program Project in Tongren University (No. 43, Application Research of Multi-user Scheduling Optimization in Wireless Powered Networks).

Acknowledgments

This research was supported by Doctoral talent project of Doctoral research project of Tongren University (trxyDH2003), by National Natural Science Foundation of China (Regional Science Foundation Project, Internet of Things Lightweight Cross-domain Authentication Security Mechanism Research) (No. 62262058), and by 2024 Undergraduate Innovation and Entrepreneurship Training Program Project in Tongren University (No. 43, Application research of multi-user scheduling optimization in wireless powered networks).

    General Statement

    Code Availability. The data will not be redistributed without permission. Contact us for permission for any other redistribution; we will consider requests for free and commercial redistribution.

    Data Availability Statement

    The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

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