Entropy Weight Method Research Articles

Coupling and Coordinated Development of Carbon Emission Efficiency in Industrial Enterprises and the Digital Economy: Empirical Evidence from Anhui, China

To delve into the interrelationship between the green transformation of industry and the economy’s high-quality development, to promote the coordinated development of industrial carbon emission efficiency and digital economy, to expand the scope and research ideas related to economic and social sustainable development, and to provide scientific reference for the low-carbon sustainable development of regional economy, this article introduced a data-centric methodology for evaluating the collaborative advancement of both industrial enterprises’ carbon emission efficiency and the digital economy. To accurately gauge the carbon footprint of industrial enterprises, models focusing on carbon emissions as well as carbon emission intensity were employed. To enhance the precision of evaluation outcomes and mitigate biases stemming from subjective weighting factors, we employed the entropy weight method to objectively assign weights to each indicator. Furthermore, the super-efficient slack-based model (SBM) can solve the problem that the conclusions are biased, due to the different radial. Subsequently, a carbon-emission efficiency slack-based measure model, and models for coupling degree and coupling-coordination degree were formulated. Anhui, as a central province in China, is also an important province in the Yangtze River Delta integration development. Coordinated development of its carbon emission efficiency and digital economy has important implications for the sustainable economy advancements of other regions in China, and even other countries or regions in the world. Therefore, Anhui was selected to be the empirical research sample. The results showed that the comprehensive levels of these two systems followed an increasing trend, while the digital economy lagged. Their coupling degree fluctuated and reached its highest point in 2021, whereas their coupling-coordination degree increased, showing high coupling and low coordination overall. This study proposes specific countermeasures and suggestions for the relevant decision-makers.

Regional differences and dynamic evolution in the resilience of public-private partnership projects: Evidence from the China

In the context of high-quality development and in the face of uncertainties and risks from economic, social and political sources, we aim to understand the resilience of public-private partnership projects in megaprojects, and to improve their ability to effectively manage adversity and optimize resource allocation. And by utilizing these insights and adopting proactive strategies, we enable stakeholders to navigate uncertainty more effectively and contribute effectively to high-quality development. Based on 2005–2020 provincial panel data in China, we explore the regional differences, dynamic evolution and convergence in the resilience of public-private partnership projects using an integrated approach combining entropy weight method, Dagum Gini coefficient, kernel density estimation and spatial econometric modeling. The results of the study show that: The resilience of public-private partnership projects in the nationwide and in East, Central, West and Northeast is steadily increasing, with higher in the East and weaker in the West; The differences between the four regions show a gradual widening trend, but decrease towards the end of the study period, and was accompanied by trailing and polarization across the country and the regions; There is absolute β convergence in the resilience of public-private partnership projects across the country and in the four regions.

Digital economy solutions towards carbon neutrality: The critical role of energy efficiency and energy structure transformation

Reducing carbon emissions is a crucial way to achieve sustainable development. Whether the digital economy helps or makes it harder to cut emissions is a matter of debate. This paper proposes a new digital economy index based on the entropy weight method. The two-way fixed effect panel model is used to analyze the data of 30 provinces in China from 2011 to 2021. The results suggest that (1) The digital economy can significantly reduce carbon emissions. (2) Improving energy efficiency and promoting energy structure transformation are two essential mechanisms for carbon reduction in the digital economy. (3) There is heterogeneity in the impact of the digital economy on carbon emissions. The digital economy significantly reduces carbon emissions from coal but does not significantly impact carbon emissions from other sources. Moreover, the emission reduction effect of the digital economy is related to regional energy endowment, intelligent manufacturing level, and industrial agglomeration level. This paper provides new empirical evidence for clarifying the relationship between the digital economy and carbon emissions and provides policy implications for promoting the realization of carbon neutrality goals.

Data-driven natural capital accounting model in Indonesia: Impacts of environmentally related economic activities on ecological processes and services

In this study, natural capital accounting (NCA) is enhanced by constructing a data-driven hierarchical model that can help improve policy decision-making. NCA enables the management and transparency of natural capital and ecological processes and services, resulting in better societal activities and well-being in Indonesia's regions; however, it does not account for environmentally related economic activities that may negatively influence natural capital, processes and services. In this study, a data-driven approach is used to address this issue. Valid attributes are identified, and a thorough NCA hierarchical model that incorporates synchronized accounting on ecology, environment-related economic activities and societal well-being is developed. This approach utilizes content and bibliographic analyses, the entropy weighted method (EWM) and the fuzzy Delphi method (FDM), which are used to validate the data-driven NCA measurements. Exploratory factor analysis (EFA) is utilized to cluster the measurements and assess their reliability. The fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to construct the NCA hierarchical structure by considering the cause-and-effect interrelationships among attributes. The valid prioritized attributes are determined, and a hierarchical structure is built to support government decision-making processes. The findings suggest that water resource accounting, forestry production, raw material extraction, resource allocation management and resource consumption management are the criteria that fluctuate the most for practitioners and governments.

New Electric Power System Stability Evaluation Based on Game Theory Combination Weighting and Improved Cloud Model

The development of new electric power system is a requirement for China to realize its “carbon peaking and carbon neutrality goals”, so it is of great importance that we assess the stability of a new electric power system under certain conditions. Firstly, according to the factors affecting the stability of the new power system, the characteristics of the new electric power system are analyzed in depth, so as to establish a stability evaluation index system including four first-level indices of safety, adequacy, flexibility, and adaptability. Secondly, a stability evaluation model is proposed. Based on the game theory, the entropy weight method, criteria importance though intercrieria correlation (CRITIC) method, and coefficient of variation method are combined and assigned, and the cloud model is improved through the combination of weights, which is used for evaluating the stability of the new electric power system. Finally, the applicability of the proposed evaluation model is verified by an arithmetic example analysis, which can identify the vulnerability of the new electric power system and provide suggestions for improving its stability. The model can provide a theoretical basis for promoting energy transition and sustainable development.

Has the national ocean economic pilot project promoted blue economy development in China?

This paper estimates the impact of the national ocean economic pilot project on the blue economy (BE) development using panel data for 11 coastal regions in mainland China from 2005 to 2017. The study shows that the BE is composed of three levels: traditional advantageous industrial construction, emerging industrial development, and ecological protection, where they can merge an index system using the analytic hierarchy process-improved entropy weight method. Besides, this paper adopts heterogeneous timing difference-in-differences to find that the policy increased the BE in the pilot areas by an average of 0.025. This effect passed various robustness checks including the parallel trend hypothesis and placebo test. In addition, the policy can also mediate the BE by skill and capacity aggregation, optimization of transportation networks, and incentives for innovation, and their contributions were 21.5%,10.6%, and 10.6%. Moreover, this paper finds that the current BE work shows a preference for the economy over the ecosystem. For economic growth, pilots prefer traditional rather than emerging industries. In summary, this study recommends that multi-level governments should use a project-based management model for the BE, increase international cooperation and knowledge transfer to promote the integration of the knowledge economy and the BE and facilitate administrative reform to clarify the ecological responsibilities of marine management agencies.

Ecuadorian Woods: Building Material Selection Using an Entropy-COPRAS Comparative Analysis Based on the Characterization of Ecuadorian Oak and Guayacan Timber.

Considering that global awareness for sustainable development has risen to face environmental damages, different building materials have been considered from a mechanical perspective. In this sense, considering the richness of South America regarding its woods, the Guayacan and the Ecuadorian oak timbers have not been previously characterized. The present research has performed mechanical, thermal, and moisture content characterizations to acknowledge the benefits of considering these materials for the building industries. In this sense, Guayacan has been shown to have lower thermal conductivity, making it ideal for thermal insulation; the oak from Manabi showed the best compressive strength; while the oak from El Oro stands with the best tensile strength; and the oak from Loja showed the best modulus of elasticity. On the other hand, all the materials were compared by multicriteria decision methods to select the best, by using the COPRAS method driven by the objective entropy-weighted method, showing that the oak from Loja is the best choice considering the advantage that presents with the modulus of elasticity. In this sense, it is concluded that regarding the mechanical properties, there is not much difference for the compression, bending, and tensile strength; nevertheless, for the modulus of elasticity the oak from Loja stands out, making it a factor to be considered in the selection of a wood for building applications that is corroborated through multicriteria decision methods.

Assessment and selection of a micro-hybrid renewable energy system for sustainable energy generation in rural areas of Zambia

This research focuses on the implementation of micro-hybrid renewable energy systems (MHRES) in rural Zambia, where a large part of the population lacks adequate electrical infrastructure. Using a multi-criterion decision-making approach (MCDM), specifically the Technique for Preference Order by Similarity to an Ideal Solution (TOPSIS) with an entropy-weighted method, this research aims to identify an optimal MHRES solution to replace dependence on wood and fossil fuels. Analysis is carried out in 14 scenarios, taking into account technical, economic, social, and environmental criteria. The results show that a biogas-solar photovoltaic integrated with Energy System Power Safe Storage Battery Systems 190 (SBS 190) is the most visible option, demonstrating a relative proximity of 0.98556. In contrast, a diesel generator-solar PV system is the least favorable, with a relative proximity of 0.02576. Validation of the results through a convergent distance assessment (CODAS), an evaluation based on the distance from the average solution (EDAS), and the weighted aggregate sum of the product assessment (WASPAS) confirm its robustness. The proposed MHRES solution is designed to meet the energy needs of rural communities using local biomass and solar resources, free of obstacles by high buildings and structures. This research highlights the potential of MHRES to meet energy needs and promote sustainable development in developing countries, providing valuable insights for policymakers and energy stakeholders.

Establishment and application of the environment evaluation model for beneficiation plant

In order to evaluate the beneficiation plant environment in a more scientific and reasonable way, this paper took the workshop environment of the beneficiation plant as the research object. This paper divided the beneficiation plant into 7 evaluation units according to its functions. The evaluation indices are dust, noise, light environment, microclimate, benzene, toluene and xylene. This paper combines the G1 method and the entropy weight method to evaluate the weight of each evaluation index, the element extension model of the concentrator working environment is established by the element analysis method, and the matter element analysis method is used to establish an evaluation index system of a beneficiation plant in East China. The results show that the evaluation level of the breaking workshop and the auxiliary facilities are unqualified, the auxiliary facility is qualified, the culling workshop, culled yard and accessory building are medium, the screening workshop and grinding workshop are good.

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Coupling Study of Green Finance and High-Quality Economic Development

Based on panel data from 16 prefecture-level cities in Shandong Province from 2012 to 2021, a comprehensive evaluation system for the development of green finance and high-quality economic development in Shandong Province was constructed. The entropy weight method was used to calculate the comprehensive index of both. The research shows that, temporally, from 2012 to 2021, the coupling degree between green finance development and high-quality economic development in the prefecture-level cities of Shandong Province was mostly at a high or relatively high level, with the coordination degree increasing year by year. However, the overall level remained low, with most cities in a state of mild or near imbalance. Spatially, only coastal cities such as Qingdao, Dongying, Weihai, and Rizhao achieved basic coordination, with the coordination degree of coastal cities being higher than that of inland cities. Among them, coastal cities mainly exhibited high-high or high-low types, while inland areas mostly showed low-low types, failing to form a good linkage effect.

Hazardous High-Energy Seismic Event Discrimination Method Based on Region Division and Identification of Main Impact Factors: A Case Study

An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications.

Investment choice of heavily polluting enterprises under green credit policy: Comparison between green and traditional paths

Green credit is an effective financial policy tool to promote green and low-carbon transformation. To analyze whether green credit constraints (GCC) can affect the investment decisions of heavily polluting enterprises (HPEs), this article studies the impact of GCC on green investment decisions of HPEs from 2013 to 2022. Based on the data of bank loan amount, interest rate and maturity of enterprises, this paper uses the entropy weight method to construct the bank loan constraint index of HPEs under green credit, and investigates the nonlinear effect of GCC on enterprises' green investment preference and its main mechanism. The results show that: (1) there is a U-shaped relationship between GCC and green investment ratio of HPEs. (2) Corporate R&D expenditure, financial asset investment, and investment term structure are the main influencing mechanisms. (3) Government environmental regulation and public environmental supervision play a positive and negative regulate role respectively. (4) The results of heterogeneity test show that green credit policy (GCP) is more significant in the eastern region, state-owned enterprises, electric power industry, chemical industry, and material industry. The research conclusions provide targeted suggestions for promoting the development of GCP and accelerating the green transformation of HPEs.

Ranking of Different States in India Based on Sustainable Women Empowerment Using MCDM Methodology Under Uncertain Environment

In this paper, women’s empowerment in different states of India is considered. Based on considered data sets, the states of India are ranked with Decision-Making (MCDM) methodology. Here, Generalized Triangular Intuitionistic Fuzzy Numbers (GTIFNs) are considered. Here, GTIFNs are taken to deal with the uncertainty and we introduce a new de-fuzzification method for converting the GTIFNs to corresponding crisp values. Here, we have applied two MCDM techniques namely the Entropy-weighted method and Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method. The entropy-weighted method is used for evaluating the criteria weights and the VIKOR method is applied to rank the alternatives. Last, to verify the stability and vagueness of the system, we perform sensitivity and comparative analysis.

Enhancing Environmental Sustainability: Risk Assessment and Management Strategies for Urban Light Pollution

Light pollution imposes significant and far-reaching adverse effects on human society, necessitating its stringent regulation. However, intervention policies could be customized to suit the unique characteristics of each region, taking into account local conditions. To address this challenge, we have developed a comprehensive light pollution risk assessment model using a combination of objective and subjective weighting methods, including analytic hierarchy process (AHP), independent weighting method (IWM), entropy weight method (EWM), coefficient of variation (CV), criteria importance through intercriteria correlation (CRITIC), and principal component analysis (PCA). This model facilitates a systematic evaluation of light pollution risk levels across diverse regions in China. Subsequently, we have proposed intervention policies targeting light pollution risk reduction and assessed their efficacy using the synthetic control method. Our findings reveal elevated light pollution risk levels in coastal and mountainous regions with heightened concentrations closer to urban centers. Strategies focused on enhancing lighting hardware, optimizing lighting schedules, and upgrading light sources demonstrated the impact on reducing light pollution risk levels (LPRL). This study not only lays a solid theoretical foundation for assessing urban light pollution risks but furnishes empirical evidence to aid relevant authorities in formulating effective light pollution control strategies.

Evaluation of Performance of Thermal Fibers and Heat Transfer Research based on Entropy Weight Method and Differential Equation

Evaluation of Performance of Thermal Fibers and Heat Transfer Research based on Entropy Weight Method and Differential Equation

Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents.

Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods.

A new two-stage emergency material distribution framework for urban rainstorm and flood disasters to promote the SDGs

Under the coupled effects of climate change and urbanization, urban rainstorm and flood has become more frequent and intense. The effect of the flood disasters emergency response has a direct impact on the success or failure of the rescue. Emergency material distribution is one of the most critical steps in the emergency response phase and is crucial to achieve the Sustainable Development Goals (SDGs). This study proposes a two-stage emergency material distribution framework for urban rainstorm and flood disasters. The entropy weight method and technique for order preference by similarity to ideal solution (EWM-TOPSIS) was adopted to incorporate the difference in relief-demand urgency into the emergency material distribution principle. The mathematical model was established by minimizing emergency travel time, inequity and social costs (logistic costs and deprivation costs) as objective functions. The gamultiobj algorithm was used to solve the model. The proposed framework is further applied in Xi'an city of China. The results showed that the affected sites were categorized into disaster risk levels I, II and III. The Pareto solution set was obtained by optimizing each conflicting objective. The emergency material distribution plan was selected from the Pareto solution set based on the actual situation. In the plan, the flood disasters emergency travel time totaled 6.48 h, the inequity totaled 3.69, and the total social costs reached 226,584 CNY. The proposed framework is more consistent with the needs of the actual rescue by comparing the four models generated plans. Decision-makers should not only consider minimizing dispatch time and social costs, but also the trade-off between equity and prioritization of distribution. This study helps to provide new ideas for sustainable urban development and emergency management. Urban rainstorm and flood disasters change with the changing environment dynamically, and it is difficult to collect dynamic disaster information on the affected sites. Thus our further study will focus on improving the prediction accuracy of dynamic urban rainstorm and flood disasters.

Comprehensive pollutant emission prediction models from hydrogen-enriched methane combustion in a gas-fired boiler based on box-behnken design method

Hydrogen-enriched methane (HEM) combustion is an effective method for reducing the consumption of carbonaceous fuels. However, high adiabatic flame temperature of H2 can cause large NO emissions. To reduce the NO and CO2 emissions from HEM combustion, the Box-Behnken design (BBD) method combining response surface experimental design, analysis of variance, and multi-nonlinear regression models was adopted. The effects of the H2 doping ratio (XH2), preheated air temperature (Tair), excess air coefficient of burners arranged at the bottom (Ex,b), and their interactions on the NO, N2O, and CO2 emissions of a gas-fired boiler were investigated. According to the BBD method, the NO, N2O, and CO2 emissions prediction models with R2 exceeding 0.98 were proposed, respectively. The results showed that the increase of Tair had a positive effect on reducing NO emissions, as the NO exhausted from HEM combustion is mainly thermal NO. Compared with Tair and Ex,b, XH2 was the most significant factor affecting N2O and CO2 emissions, indicating that an increase in XH2 can significantly reduce greenhouse gas emissions. Additionally, the formation and consumption of N2O emissions presented a competitive mechanism at XH2 increased from 0 to 0.8, and CO2 emissions reduced with the increase of XH2, Tair, and Ex,b. Furthermore, a comprehensive emission prediction model with NO, N2O, and CO2 emissions was proposed. With the target of minimizing total NO, N2O and CO2 emissions, three groups of combinations for XH2, Tair, and Ex,b were obtained from the equal weight, entropy weight method, and criteria importance through the inter-criteria correlation method as 1:1:1, 24:9:17, and 21:11:18. The results showed that for minimize emissions, there is not much difference in the results calculated based on the optimal boundary conditions under different weights. For various reduction targets for nitrogen, carbon, and greenhouse gases, three groups of optimal combinations of XH2, Tair, and Ex,b were obtained and their accuracies were verified based on numerical calculations with relative errors below 6%. The proposed comprehensive pollutant emission prediction models are conducive to calculating the pollutant emissions of HEM combustion and guiding the investigation of prediction models considering other operating conditions.

Optimization and Evaluation of Tourism Mascot Design Based on Analytic Hierarchy Process-Entropy Weight Method.

With the tourism industry continuing to boom, the importance of tourism mascots in promoting and publicizing tourism destinations is becoming increasingly prominent. Three core dimensions, market trend, appearance design, and audience feedback, are numerically investigated for deeply iterating tourism mascot design. Further, a subjective and objective evaluation weighting model based on the hierarchical analysis method (AHP) and entropy weighting method is proposed, aiming to utilize the advantages of these methods and ensure the entireness and correctness of results. Taking the mascots of six famous tourist attractions in Xi'an as an example, the feasibility and effectiveness of the evaluation model are verified. Data analysis and modeling results confirm that the three core evaluation indexes of scalability, innovation, and recommendation should be focused on in the design of tourism mascots in the three dimensions of market trends, appearance design, and audience feedback. The evaluation index scores are 0.1235, 0.1170, and 0.1123, respectively, which further illustrates the priority of mascot design. The evaluation model constructed by the research provides decision-makers with a comprehensive evaluation tool from the perspective of tourist experience, and also effectively assists the optimization process of mascot design. In addition, the model has good versatility and adaptability in structural design and evaluation logic and can be widely used in the optimization and evaluation research of brand mascots.