Entropy Weight Research Articles

Coupling and coordination relationship of tourism inclusive green growth system: Evidence from Shandong Province

Sustainable growth is a prerequisite for high-quality development, and inclusive green growth is regarded as an important way to realize sustainable growth. This paper constructs a tourism inclusive green growth index (IGGI) system based on the 2018 Asian Development Bank IGGI. Using Shandong Province from 2017 to 2019, the entropy weight method is utilized to explore the degree of coupling and coordination among the subsystems of tourism inclusive green growth. The study shows that: (1) the tourism IGGI continues to grow; (2) in the tourism inclusive green growth system, the social equity subsystem shows a trend of steady improvement;and (3) in the system, there is a slight discordance between the economic growth subsystem and the environmental sustainability subsystem. The study aims to provide the scientific basis for the high-quality development of tourism and theoretical support for the field of tourism and environmental sustainability.

Spatio-temporal evaluation of ecological security of cultivated land in China based on DPSIR-entropy weight TOPSIS model and analysis of obstacle factors

Cultivated land’s ecological security ensures human well-being and is critical to global food security. However, the unchecked expansion and excessive exploitation of agricultural resources have led to considerable land degradation issues in China, including soil erosion, contamination, and environmental degradation, raising concerns over the cultivated land ecological security (CLES). Current research on cultivated land (CLES is relatively limited, particularly at the national level across different provinces. In addition, a unified and comprehensive CLES evaluation index system has yet to be formed. To fill this research gap, we construct a CLES evaluation system with 35 indicators from three dimensions of “society-natural-economy” based on the DPSIR model framework and entropy weight TOPSIS model. Simultaneously, we conducted a spatial–temporal analysis of CLES in 31 provinces of China from 2012 to 2021 and applied the obstacle degree model to analyze the main factors impeding the CLES in China. The results showed that: (1) During the study period, the CLES and its subsystems showed a good development trend. Spatially, the CLES in China presents a regional difference of “high in the east and low in the west” and a significant regional difference among provinces. (2) The spatial CLES in different provinces of China presents a significant positive correlation, and the spatial clustering effect of CLES is gradually strengthening. The “High-High” clusters are mainly distributed in the northeast and eastern regions, while the “Low-Low” clusters are mainly located in the western region. (3) From the perspective of the criterion level, the state subsystem and the impact subsystem are the main obstacles hindering the improvement of CLES in China. At the indicator level, the common obstacle factors affecting the enhancement of CLES across provinces in China include soil erosion control intensity, number of green food certifications, per capita agricultural loans, per capita cultivated land area, and per capita grain production. Our research results can provide practical policy reference for further improving the ecological protection of cultivated land.

A three-stage evaluation model for biomass co-firing combined with carbon capture and storage (in oil wells) retrofit in coal-fired power plants based on multi-criteria decision-making: An example from Hebei Province, China

Coal-fired power plants can be retrofitted by biomass co-firing combined with CO2 capture and storage, reducing carbon emissions significantly. Site selection is an important task for the entire project, but little research has been done on this. Therefore, this paper proposed a three-level evaluation model. Firstly, alternative power plants and carbon sequestration sites (set oil wells as an example) were screened by feasibility evaluations. Secondly, source-sink pairs were performed based on the shortest distance between power plants and oil wells. Finally, source-sink pairs were evaluated with suitability evaluation. Entropy weight method (EWM) and Analytic hierarchy process (AHP) were used to calculate objective and subjective weights, respectively. Lagrangian optimization method was used to combine the two weights. The ranking results of alternative source-sink pairs were obtained through Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) improved by combined weight. An example of in-service power plants in Hebei Province was conducted. Engineering geology and reservoir characteristics were the most important considerations. D5 (source: Langfang thermal power plant, sink: Bieguzhuang - Jing 11 oil well) was the best choice of the five given candidates. The results of the study provided an evaluation framework for governments and investors in retrofitting power plants.

Energy efficiency evaluation of wind turbines based on entropy weight method and stacked autoencoder

Abstract To solve the problems of complex operating conditions of wind turbines and difficult to measure and evaluate energy efficiency, a quantitative evaluation method of energy efficiency of wind turbines based on the entropy weight method (EWM) and stacked automatic encoder (SAE) was proposed. Firstly, based on the Supervisory Control And Data Acquisition (SCADA) data of wind turbines, key evaluation indicators are extracted through preprocessing and feature extraction of wind turbine data. The EWM is used to determine the weights, and deep learning is combined with SAE to comprehensively obtain the energy efficiency value of wind turbines. Subsequently, the quartile method was used to determine the discrimination threshold for inefficient units, and the effectiveness of this method on SCADA data of a 5.5MW offshore wind farm was verified through numerical examples. The results show that this method can evaluate the energy efficiency level of wind turbines in real time and achieve 100% accuracy in identifying inefficient units, providing a reference for the maintenance and technical improvement of wind turbine energy efficiency performance.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin, China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment. Industrialization and urbanization promote social-economic development, but they also have generated a series of environmental and ecological issues in this basin. Previous researches have evaluated urban resilience at the national, regional, urban agglomeration, city, and prefecture levels, but not at the watershed level. To address this research gap and elevate the Yellow River Basin’s urban resilience level, we constructed an urban resilience evaluation index system from five dimensions: industrial resilience, social resilience, environmental resilience, technological resilience, and organizational resilience. The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin. The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010, 2015, and 2020. Furthermore, the grey correlation analysis method was utilized to explore the influencing factors of these differences. The results of this study are as follows: (1) the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015, and significant spatial distribution differences were observed, with a higher resilience level in the eastern region and a low-medium resilience level in the western region; (2) the differences in urban resilience were noticeable, with industrial resilience and social resilience being relatively highly developed, whereas organizational resilience and environmental resilience were relatively weak; and (3) the correlation ranking of resilience influencing factors was as follows: science and technology level>administrative power>openness>market forces. This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Coupling coordination between electricity and economy: China as an example

The benign coupling coordination between electricity (EL) and economy (EC) contributes to a better environment and sustainability. This study explores whether EL and EC can coordinate theoretically, how to evaluate their coordination, what the statuses are, and how to enhance coupling coordination levels (CCL). Specifically, we select the data from 2011 to 2020 of the 31 provincial regions of China, use information entropy weight and the technique for order preference by similarity to an ideal solution method, establish the theoretical coupling coordination mechanism and the evaluation index system to measure CCL temporally-spatially and propose policy implications based on prediction tendencies. We find that most regions' CCLs fluctuate temporally with mild upward trends, indicating the much more benign coupling coordination status; the spatial distributions are uneven with narrowing gaps. However, future CCL gaps may increase; therefore, differentiated policy implications are needed, such as encouraging balanced-coordination policies, innovating for higher-quality coordination, and cooperating for intelligence and wealth transfer. This study is conducive to theoretically describing the coupling coordination mechanism between EL and EC, providing new insights for CCL evaluation and the coordination practice for different regions.

Building Resilient Food Security Against Global Crisis: New Evidence From China

ABSTRACTThe existing literature addresses the importance of food system disruptions and the risk of the global food crisis. However, there is insufficient understanding of response strategies and their effectiveness evaluations. This study offers a comprehensive introduction to China's food security policies and evaluates their effectiveness in enhancing the nation's risk resistance capability. Utilizing the Entropy Weight Method (EWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we evaluated China's provincial food security resilience (FSR) from 2003 to 2020 and adopted the ArcGIS platform to map spatiotemporal trends. Our findings reveal significant improvements in FSR nationwide, with a notable average annual growth rate of 1%–5%. However, regional disparities persist, with higher resilience observed in eastern provinces compared to the west. The study emphasizes the effectiveness of China's food security policies, which have synergistically enhanced grain production, agricultural mechanization, and farmers’ economic conditions. The article offers policy recommendations aimed at bolstering China's FSR and challenges with global implications. Our study contributes to the broader discourse on global food security by offering a nuanced understanding of the effectiveness of policy interventions in a major agricultural economy.

Relationship Research between Digital Economy and High-Quality Economic Development on the view of Foreign Trade Opening

Digital Economy (DE), as a vital impetus for high-level openness, interacts with High-Quality Economic Development (HQED). Researching Coordination mechanism among these three aspects carries substantial weight in a country to achieve common prosperity and strategic goals. Based on this premise, this paper first uses provincial data from 2014-2021 as the research object, measuring the levels of Digital Economy and HQED Employing the comprehensive index, Entropy Weight Method and critic weight. It also employs the coefficient of variation, Theil index, and coefficient of dispersion to analyze the trends in regional disparities concerning the development of DE and HQED. Subsequently, the Dynamic interaction model, grounded in the entropy weight method, was employed to quantitatively assess the coupling coordination between DE and HQED. Then, the random forest model was used to explore the impact of constituent indicators regarding the coupling coordination between the two systems, with XGboost and LASSO models serving as supportive evidence. Empirical analysis results indicate that, on one hand, the disparities in HQED among regions showed an initial increase followed by a decrease, while the disparities in Digital Economy development among regions demonstrated a year-over-year decreasing trend. On the other hand, during 2014-2021, the disparities in harmony with DE and HQED among regions decreased annually. Additionally, foreign-trade dependence and share of foreign investment are found to be significantly important to Coupling efficiency.

Optimization of AC contactor closing phase angle based on entropy weight method and efficacy coefficient method

Optimization of AC contactor closing phase angle based on entropy weight method and efficacy coefficient method

Research on Sales of New Energy Vehicles Based on ARIMA Model

Firstly, this article identifies seven indicators based on electric vehicles: battery endurance, number of public charging facilities, government subsidies, gasoline prices, electricity prices, carbon emissions, and number of power battery companies. Spearman correlation analysis is conducted between indicator data and sales of new energy electric vehicles. The correlation coefficients of the first five indicators are all greater than 0.6, which is the main influencing factor. Then, the sales of new energy vehicles are predicted using a yearly forecasting method. Due to the close correlation between the sales of new energy vehicles and the five main indicators, the ARIMA time series model is first used to predict the annual quantity of the five indicators. Then, using these five indicators as inputs and sales as outputs, a BP neural network model is established to predict the annual sales of new energy vehicles in China to be 46051748 units in ten years. Finally, quantifying the score of ecological environment assessment using four evaluation indicators: sulfur dioxide, smoke emissions, ammonia nitrogen, and carbon dioxide. The entropy weight method was used to determine the weights of each indicator, and the results were 37.25%, 30.96%, 21.24%, and 10.55%, respectively. Then apply it to the TOPSIS model to obtain ecological environment scores for different years, and analyze the correlation between the degree of electrification and ecological environment scores. Finally, it can be concluded that the larger the market share of new energy vehicles, the better the ecological environment.

Effect of Combined Application of Wood Vinegar Solution and Biochar on Saline Soil Properties and Cotton Stress Tolerance.

Biomass pyrolysis by-products, such as biochar (BC) and wood vinegar (WV), are widely used as soil conditioners and efficiency enhancers in agriculture. A pot experiment was conducted to examine the effects of WV, both alone and in combination with BC, on soil properties in mildly saline soil and on cotton stress tolerance. The results demonstrated that BC and WV application, either individually or together, increased soil nutrient content. The combined application was more effective than the individual applications, resulting in a 5.18-20.12% increase in organic matter, a 2.65-15.04% increase in hydrolysable nitrogen, a 2.23-58.05% increase in effective phosphorus, and a 2.71-29.38% increase in quick-acting potassium. Additionally, the combined application of WV and BC led to greater improvements in cotton plant height, net photosynthetic rate (Pn), leaf nitrate reductase (NR), superoxide dismutase (SOD), and catalase (CAT) activities compared to the application of BC or WV alone. The enhancements in this study varied across different parameters. Plant height showed an increase of 14.32-21.90%. Net photosynthetic rate improved by 13.56-17.60%. Leaf nitrate reductase increased by 5.47-37.79%. Superoxide dismutase and catalase showed improvements of 5.82-64.95% and 10.36-71.40%, respectively (p < 0.05). Moreover, the combined treatment outperformed the individual applications of WV and BC, resulting in a significant decrease in MDA levels by 2.47-51.72% over the experimental period. This combined treatment ultimately enhanced cotton stress tolerance. Using the entropy weight method to analyze the results, it was concluded that the combined application of WV and BC could enhance soil properties in mildly saline soils, increase cotton resistance, and hold significant potential for widespread application.

Multi-objective optimization of L-shaped fins in rectangular phase change energy storage units based on genetic algorithm

Phase change energy storage technology holds immense potential in the field of energy storage, and enhancing the efficiency of energy storage systems has long been a focal point of industry attention. This study aims to optimize the design of an L-shaped fin structure to improve the melting rate of the phase change material (PCM) within a rectangular phase change energy storage unit and enhance the overall system performance. Through the utilization of numerical simulations and the response surface method (RSM), the influence of fin design parameters - specifically, the length and thickness of the main segment and the length and thickness of the branch segment - on the energy storage per unit mass (Em) and energy storage efficiency (Pt) of the energy storage unit is evaluated. The results reveal that the length of both the main and branch segments of the L-shaped fin significantly impacts the melting rate of the PCM, whereas the thickness of these segments has a comparatively lesser effect. Additionally, when the length of the main segment reaches a sufficient value, increasing the length of the branch segment effectively addresses the challenge of PCM melting difficulties at the bottom of the rectangular phase change cavity caused by natural convection. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is employed for the multi-objective optimization of the L-shaped fin shape, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with entropy weighting is applied for decision-making to determine the optimal solution. Notably, when the weights of Em and Pt are set at 44.9 % and 55.1 % respectively, the overall performance of the energy storage unit is optimized. The Pareto-optimal decision solution exhibits a significant improvement of nearly threefold in Pt compared to the case without fins, while the reduction in Em is only 5.24 %. This design approach presents a novel perspective for determining fin parameters in phase change energy storage devices and is expected to drive advancements in phase change energy storage technology.

Research on Benefit Evaluation and Optimization Strategy of Rural Ecological Products Value Realization

As the cornerstone of human existence and progress, ecological products indispensably support the sustainable development of society. In order to deeply discuss the benefits and achievements of eco-product value realization in various regions, this paper sorts out the research status of the evaluation system and method of rural eco-product value realization in Pukou District, and constructs an evaluation system of eco-product value realization benefit including 11 indicators of three subsystems: ecological benefit, economic benefit and social benefit. Using entropy weight method to measure the value realization benefit of rural ecological products. The results show that: (1) the overall development level of Pukou District is relatively high, and the work of promoting the realization of the value of rural ecological products is in good condition, and the overall development is in the growth stage; (2) From 2017 to 2022, the comprehensive benefits of ecological products in Pukou District have been steadily improved, and the comprehensive benefits have increased from 0.385 to 0.642; (3) Ecological benefits increased significantly, followed by economic benefits, and social benefits increased in a downward trend.

Optimizing Fertilization Strategies to Promote Leaf-Use Ginkgo Productivity and Ecosystem Economic Benefits: An Integrated Evaluation of a Field Trial in Southern China

Field experiments were conducted on a four-year-old leaf-use ginkgo plantation in southern China to assess the impact of nine different fertilization strategies with varying N-P2O5-K2O rates at three growth phases (FBD: March for bud development; FLG: May for leaf growth; FLS: July for leaf strengthening) on leaf-use ginkgo (Ginkgo biloba L.) leaf productivity and ecological economic benefits (EEBs). The results indicated that regardless of timing and rate, fertilizer application led to an increase in leaf area and thickness, resulting in higher ginkgo leaf yield. The highest fresh (215.14 g tree−1) and dry (78.83 g tree−1) yields were observed with 3 g N + 2.5 g P2O5 + 1.5 g K2O tree−1 in FLG. FLS was found to mitigate the decline in SPAD values of leaves during late summer. Furthermore, fertilized ginkgo trees exhibited higher flavonoid concentrations in leaves, enhancing profitability. However, higher fertilizer rates were associated with elevated greenhouse gas emissions, nitrogen losses and ecological costs. Despite these drawbacks, all fertilization treatments resulted in increased net economic income. Specifically, compared to no fertilization, FBD, FLG and FLS treatments boosted net income by 3.5~26.6%, 11.6~60.5% and 5.8~35.4%, respectively. Using the entropy weight TOPSIS method, it was concluded that optimizing the N, P and K fertilization rate and timing (applying 3–2.5–1.5 g tree−1 of N-P2O5-K2O in May) is a beneficial approach to maximize EEBs and industrial benefits in leaf-use ginkgo plantations in southern China. This study provides valuable insights into suitable fertilization patterns and management for leaf-use ginkgo plantations in southern China.

A novel mass integration cogeneration system: Working fluid selection model and multi-criteria decision analysis

As the globe faces major difficulties such as climate change, environmental pollution, and limited energy resources, improving energy efficiency and decreasing the environmental effects of energy systems have become research priorities. A novel mass integration cogeneration (MICHP) system is proposed in this paper. In addition, a mathematical model relating the basic physical properties of the working fluid to the system's performance has been developed. R1233zd(E), R1224yd(Z), R601a, R245fa, and acetone have been chosen to be applied to the MICHP to confirm the model's accuracy. By utilizing the technique for order preference by similarity to an ideal solution, the entropy weight method, and energy, exergy, economic, and environmental analyses of the MICHP, the general efficiency of the working fluid is further explored. The results show that the intermediate heat exchanger is the largest exergy destruction equipment in the system. R601a has the highest return on investment of 0.53, and the annual operating cost is found to be proportional to the latent heat of the working fluid. R1233zd(E) has the lowest environmental impact, with a total equivalent warming impact of −4.89 × 106 kgCO2eq. The multi-criteria decision analysis of the working fluid agrees with the order of the proposed mathematical model's results, proving the model's accuracy. Consequently, the mathematical model can provide a helpful technique guide for working fluid selection in cogeneration systems.

Data Source Importance Evaluation for Highway Networks: A Complex Network-Based Approach

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Comprehensive vulnerability assessment for environmental facility depending on spatial characteristics in South Korea

Among environmental facilities, wastewater treatment facilities have a crucial role in sustaining human life, and any occurrence of an earthquake or flood within these facilities can result in various social, economic, and environmental issues, either directly or indirectly. Therefore, a quantitative vulnerability assessment of wastewater treatment facilities is necessary to minimize and prevent damage from earthquakes and flood disasters. For this reason, this study introduces a novel indicator to assess the susceptibility of disasters, considering aspects of exposure, sensitivity, and adaptive capacity. The newly proposed indicator encompasses numerous evaluation criteria, topography, natural surroundings, hydraulic systems, structural composition, and non-structural features. Also, Weights derived using the combined weight calculation (CWC) method, which combined the analytic hierarchy process (AHP) and entropy weight method were applied to the indicator. It was tested across 23 cities to validate its efficacy, revealing a substantial correlation between the vulnerability index and the specific attributes of the city's wastewater treatment facilities. Therefore, this study analyzed wastewater treatment facilities by comparing the attributes of the urban areas under investigation, such as topological characteristics, urbanization levels, population density, infrastructure quality, and disaster preparedness resources available. The suggested methodology can facilitate the development of strategies aimed at averting damage caused by earthquakes or floods and reducing the adverse impact on wastewater treatment facilities while considering the unique characteristics of the urban setting in question.

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

- Accurate state of health (SOH) of batteries is a crucial prerequisite for ensuring the safety and stable operation of electric vehicles. However, existing conventional prediction methods do not consider the influence of early-stage capacity variations on full-cycle SOH with extensive test data. This paper develops a SOH early prediction method of lithium-ion batteries based on voltage interval selection and features fusion. To identify the battery SOH curves with high similarity under identical charge-discharge conditions, a double correlation based early-stage SOH similarity analysis method is presented. To minimize the amount of voltage training data collected during feature extraction, a voltage interval selection method considering sampling time and features correlation is introduced. Meanwhile, a feature fusion method combining entropy weight and correlation factor is used to reduce the impact of redundant health feature information. To address the problems of low accuracy and computational inefficiency using the least squares support vector machine (LSSVM) model, a grey wolf optimizer-LSSVM-adaptive boosting model is developed for battery SOH prediction. The experimental results show that the coefficients of determination remain above 0.98, indicating high SOH prediction accuracy using the developed method. Compared to other methods, the mean absolute errors based on the developed method are maintained below 1.5 %.

Water–Energy–Food Nexus in the Yellow River Basin of China under the Influence of Multiple Policies

The water–energy–food (WEF) nexus constitutes a pivotal aspect of regional ecological protection and high-quality development. The exertion of multiple WEF-related policies would engender both synergies and trade-offs within the WEF nexus. However, a quantified framework that integrates the impact of multiple WEF-related policies with conventional WEF nexus assessments and simulations is currently lacking. This study quantified the WEF nexus in the Yellow River basin (YRB) of China under the influence of multiple policies, calculated the current and future WEF scores under different policy combination scenarios using the improved entropy weight method, the auto-regressive integrated moving average (ARIMA) model, and the linear optimization method. The results revealed the following: (1) From 2000 to 2020, WEF overall scores and subsystem scores were substantially increased with spatial heterogeneity. (2) Scenario analysis indicated that policy implementation would generally accelerate WEF score improvements in each city, yet embracing all policies simultaneously was not optimal for each city. (3) The spatial heterogeneity in policy impacts was also found in the YRB, with higher trade-offs in the upper reaches of cities, and higher synergies in the middle and lower reaches of cities. To attain high-quality development within the YRB, the related policies’ implementation should consider the regional disparities and enhance the optimization of resource allocation across the regions.

CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models

Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions.