Search Results (1,750)

Based on the theories of symbiosis and configurational analysis, this study constructs a theoretical framework for exploring the sustainable development of tea agricultural heritage systems, with an empirical investigation of 40 typical cases in China. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) and integrating multi-source data, this study delves into the intricate mechanisms underlying its sustainable development. The findings indicate that the sustainable development of tea agricultural heritage systems is not determined by a single factor but results from the interplay of multiple conditions. Specifically, ecological protection performance and regional driving capacity serve as necessary conditions, while research resource allocation, industrial comprehensive strength, and heritage site development level act as sufficient conditions. Furthermore, the sustainable development pathways can be categorized into two types, namely “dual-cycle drive” and “total-factor drive”, encompassing four configurations. The “dual-cycle drive” emphasizes the mutually beneficial symbiosis between ecological and socio-economic sustainability, involving ecological protection, research resources, regional driving capacity, and industrial strength. The “total-factor drive”, on the other hand, reflects the synergistic symbiosis of ecology, socio-economy, and culture, incorporating various combinations of factors such as ecological protection, regional driving capacity, tea culture inheritance, and heritage site development. Lastly, the driving combinations leading to non-sustainable development exhibit asymmetry, suggesting that the formation of non-sustainability is not merely the reverse outcome of sustainable conditions. The absence of key conditions, such as ecological protection or regional driving capacity, results in the emergence of non-sustainable configurations. In conclusion, this study unveils the complexity and multidimensionality of the sustainable development of tea agricultural heritage systems, providing a scientific basis and practical pathways for formulating effective protection and sustainable development strategies. Full article

The loss of power grid efficiency is affected by a number of factors, including the physical characteristics of the equipment used, the operational characteristics of the power grid itself, the characteristics of the electricity being used, and the natural conditions present. The application of a simple line loss rate value is inadequate for the objective evaluation of the implementation of line loss management in different provinces. Accordingly, this paper puts forth an innovative comprehensive evaluation method for municipal power grid line loss management, namely the construction of a comprehensive management evaluation knowledge graph for line loss. This method integrates the dimensionality reduction capability of nuclear principal component analysis with the combined decision support capability of DEMATEL-Entropy Weight-TOPSIS, thereby achieving a comprehensive evaluation of power grid line loss in the whole process and across multiple dimensions. The empirical analysis of cross-sectional data based on line loss management in Guangxi from 2014 to 2016 demonstrates that the line loss evaluation index system proposed in this paper effectively reduces the redundancy of line loss data. To illustrate, the municipal power grid in Guangxi (GL city) has demonstrated a gradual enhancement in the competitiveness of its management and planning loss reduction strategies. However, there has been a notable decline in the competitiveness of its operational and technical loss reduction approaches, with a reduction of approximately 23.7%. Therefore, the overall development of the city’s power grid line loss level can be considered stable. This method allows for an objective and reasonable reflection of the actual situation differences in line loss management work, thus providing a basis for future line loss management evaluation. Full article

The urgency of mitigating climate change and the challenges it poses to ecosystems and human systems are highlighted in the Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report (AR6). In order to achieve sustainable development, it is imperative to adopt a series of adaptive measures to enhance the resilience of various sectors to climate change and reduce greenhouse gas emissions. This article analyzes the driving mechanism behind the reduction in electricity consumption by urban residents based on 302 valid questionnaires from 18 communities in nine districts in B City. Using a method that combines qualitative and empirical research, the study proposes policy recommendations aimed at guiding urban residents toward reducing their electricity consumption. These recommendations serve as a policy reference for cities striving to achieve sustainability and low-carbon targets. The primary innovations and conclusions of the study are as follows: (1) this study summarizes the primary factors and processes influencing the reduction in electricity consumption among urban residents, examined from the following three perspectives: residents’ characteristics, psychological understanding, and external environment. (2) On the basis of the research data, empirical analysis and hypothesis testing are conducted using a variety of mathematical and statistical methods. The results indicate significant differences in the electricity consumption reduction behavior of heterogeneous urban residents in both public and private areas. Subjective norms, perceived behavioral control, and knowledge of electricity conservation have significant direct influences on residents’ willingness to reduce their electricity consumption. Among these factors, subjective norms have the most significant impact, while the impact of attitude is negligible. Economic incentive policies have a significant positive regulatory effect on the relationship between “willingness (intention)” and “private area electricity consumption reduction behavior”. Full article

The combustion chamber structure of a rotary engine involves a combination of interacting parameters that are simultaneously constrained by engine size, compression ratio, machining, and strength. It is more difficult to study the weight of the effect of the combustion chamber structure on the engine performance using traditional linear methods, and it is not possible to find the combination of structural parameters that has the greatest effect on the engine performance under the constraints. This makes it impossible to optimize the combustion chamber structure of a rotary engine by focusing on important structural parameters; it can only be optimized based on all structural parameters. In order to solve the above problems, this paper proposes a method of dimensionality reduction for the structural parameters of a combustion chamber based on active subspace and combining a probability box and the EDF (Empirical Distribution Function). This method uses engine performance indexes such as explosion pressure, maximum cylinder temperature, and indicated average effective pressure as the influence proportion analysis targets and quantitatively analyzes the influence proportion of combustion chamber structure parameters on engine performance. Eight main structural parameters with an influence of more than 85% on the engine performance indexes were obtained, on the basis of which three important structural parameters with an influence of more than 45% on the engine performance indexes and three adjustable structural parameters with an influence of less than 15% on the engine performance indexes were determined. This quantitative analysis work provides an optimization direction for the further optimization of the combustion chamber structure in the future. Full article

Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R², MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model. Full article

Digital transformation is spreading from urban to rural areas, and the construction of digital villages has become a key growth point for rural sustainable development globally. Digital villages improve the level of basic public services delivery in rural areas via the penetration of digital technology. However, few studies have empirically investigated the theoretical mechanisms underlying the impact of digital villages on various aspects of rural basic public services. To address these gaps, this study investigates the impact mechanisms of digital villages on rural basic public services delivery in terms of accessibility, equity, agility, holistic nature and participation. Using 1840 counties in China as the research sample, this study applies the entropy method to extract a composite index of basic public services and performs correlation, regression, and heterogeneity analyses to examine the effects of digital villages on basic public services delivery. Empirical analysis results show that the construction of digital villages is positively associated with the level of basic public services in rural areas. Meanwhile, heterogeneity analysis results confirms that this relationship is mainly observed in eastern counties but not observed in central and western counties. These findings provide a basis for using digital inclusion to improve basic public services delivery in rural areas and achieve balanced development across regions. Full article

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China’s NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government’s public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation. Full article

Asymmetrical V-shaped tunnels often appear in tunnels crossing the river or urban underground road tunnels. The smoke flow inside is affected by a lot of factors. A full understanding of the smoke flow in this kind of tunnel is the basis of the smoke control. In this study, the effects of slope composition and fire heat release rate (HRR) on the longitudinal induced airflow velocity, the smoke back-layering length at the small slope side, and the maximum ceiling temperature were studied by the numerical method. The results show that when the fire occurs at the slope change point of the V-shaped tunnel, the maximum ceiling temperature decreases with the increase in the slope of the large-slope side tunnel. The longitudinally induced velocity is primarily related to the slope of the large-slope side tunnel and the fire HRR. When the slope difference between the side tunnels or the slope of the large-slope side tunnel is large, the smoke in the small-slope side tunnel flows back toward the fire source after reaching its maximum dispersion distance and then reaches a quasi-steady state. The smoke back-layering length is mainly affected by the slope and length of the large-slope side tunnel. When the slope of the large-slope side tunnel is 9%, the induced airflow velocity from the small-slope side can prevent the spread of smoke. The empirical models of the smoke back-layering length and the longitudinal induced airflow velocity in the small-slope side tunnel are drawn, respectively, by the theoretical analysis and the numerical results. This study can provide technical support for the design and operation of smoke control systems in V-shaped tunnels. Full article

These days, the use of natural materials is required for sustainable and consequently plus-, zero- and low-energy construction. One of the main objectives of this research was to demonstrate that pelite concrete block masonry can be a structural and thermal insulation material. In order to determine the actual thermal insulation parameters of the building partition, in situ experimental research was carried out in real conditions, taking into account the temperature distribution at different heights of the partition. Empirical measurements were made at five designated heights of the partition with temperature and humidity parameters varying over time. The described experiment was intended to verify the technical parameters of perlite concrete in terms of its thermal insulation properties as a construction material used for vertical partitions. It was shown on the basis of the results obtained that the masonry made of perlite concrete blocks with dimensions of 24 × 24.5 × 37.5 cm laid on the mounting foam can be treated as a building element that meets both the structural and thermal insulation requirements of vertical single-layer partitions. However, it is important for the material to work in a dry environment, since, as shown, a wet perlite block has twice the thermal conductivity coefficient. The results of the measurements were confirmed, for they were known from the physics of buildings, the general principles of the formation of heat and the moisture flow in the analysed masonry of a perlite block. Illustrating this regularity is shown from the course of temperature and moisture in the walls. The proposed new building material is an alternative to walls with a layer of thermal insulation made of materials such as polystyrene or wool and fits into the concept of sustainable construction, acting against climate change, reducing building operating costs, improving living and working conditions as well as fulfilling international obligations regarding environmental goals. Full article

The environmental problems caused by carbon emission have become the focus of worldwide attention. Effective control of carbon emissions cannot be achieved without the protection of the rule of law. Environment public interests litigation is a prominent innovation in the judicial system, and its role in supervising the government to perform its regulatory duties on carbon reduction and regulating the carbon emission behaviors of enterprises and the public deserves discussion. The paper selected the panel data from 274 prefecture-level cities from 2013 to 2021 and analyzed the impact of a procuratorial public interest litigation pilot policy on carbon emission control by using the double difference method. The research found that the procuratorial public interest litigation pilot policy can effectively curb carbon emissions. Heterogeneity analysis showed that in cities with relatively low level of green innovation, the negative correlation between procuratorial public interest litigation pilot policies and carbon emissions is more significant. Compared with the eastern region, in the central and western regions, especially in the central region, where the concept, policy, and funding of carbon emission governance are relatively weak, the implementation of the pilot policy of procuratorial public interest litigation had a more obvious effect on carbon emission governance. Mechanism tests showed that procuratorial public interest litigation policies reduce carbon emissions by reducing energy consumption and increasing public participation in environmental protection. The study will provide an empirical basis for the carbon emission reduction effect on pilot policy of procuratorial public interest litigation and will offer certain theoretical recommendations for improving the procuratorial public interest litigation system in the ecological environment field. Full article

Container transportation has the advantages of standardization, high efficiency, and high safety, which are essential for promoting the development of the world economy and trade. Emergencies such as severe weather, public health incidents, and social security incidents can negatively affect the operational reliability of the container shipping network. To ensure the network security and high-quality operation of container shipping, a double-layer coupled container transportation network is first described to analyze the evolution of the container shipping network and the risk propagation dynamics of operation participants. On this basis, a cascade failure model of the container shipping network considering risk level is constructed. To evaluate the vulnerability of the container shipping network, the transmission mechanism of cascade failure effects of the container shipping network under different emergency development trends and the evolution law and influence path of the container shipping network structure are both analyzed. Finally, we empirically studied the container shipping network in China and the United States, and characteristic parameters of the China–U.S. container shipping network are calculated and analyzed. The model’s validity is verified through practical cases and model simulation results, and the cascading failure process of the container shipping network in China and the United States under three types of attacks is simulated. Suggestions are provided for effective improvement in the vulnerability of the container shipping network under every kind of contingency. Full article

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities. Full article

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined. Full article

Pipeline pullback load is a crucial basis for drill rig selection and pipeline strength design. This paper presents a new pullback load calculation model from the perspective of pipe–borehole wall contact. The pipe–borehole wall contact analysis includes the distribution of contact pressure and the relationship between the external load and compressive displacement. The friction force between the pipe and the borehole wall was calculated based on the pipe–borehole wall contact analysis and adhesion theory without depending on the empirical friction coefficient. The effects of the eccentricity were also considered when calculating the fluid drag force. Through case studies, we verified the applicability of the model and discussed the possible reasons for the errors between the theoretical and field-measured results. This study can provide a helpful tool for analyzing the pipe–borehole wall contact and pullback loads for horizontal directional drilling. Full article

Investments form the basis for high-quality economic growth by ensuring renewal of production capacities and improvement of technologies and processes, thereby increasing labor productivity. Investment research is key to understanding its impact on the country’s economic advancement and more effective government policymaking to stimulate investment activity. The purpose of this paper is to study the relationship between fixed capital and R&D investments and labor productivity growth, as well as to determine the optimal level of these investments in determining the greatest effect on labor productivity growth. We use a regression model and the least squares method to empirically analyze data for seven countries for the period between 1997 and 2022. We calculate estimated values using SPSS and Python. The results show a certain impact of fixed assets and R&D investments on labor productivity growth. However, the payoff varies across the countries under study. Furthermore, despite its relatively small volumes, investment in R&D brings a greater effect on productivity growth than investment in fixed capital. With other factors remaining constant, the calculated optimums for investments in fixed assets and R&D show maximum points of growth in labor productivity. Full article