Sustainable Development Research

Research on the Construction and Planning of Industrial Complexes in Scenic Areas

Lin Zhou, Hu Chen — Fri, 28 Mar 2025 00:00:00 +0800

With the stable development of the social economy and the continuous improvement of living standards, people increasingly pursue spiritual fulfillment through tourism. Under the current national push for high-quality tourism development and the comprehensive implementation of rural revitalization strategies, while tourism resources flourish, the development of surrounding towns and villages often lags behind scenic areas, creating disparities. By studying the construction and planning of industrial complexes in scenic areas, we can drive further economic development in neighboring towns and villages, increase income, and enhance the comprehensive competitiveness of scenic areas. This study focuses on the Baili Rhododendron Scenic Area, integrating spatial layout optimization, ecological conservation, and cultural inheritance into a holistic planning framework. It explores the "industrial complex" model to achieve natural ecological protection, deep industrial integration, and balanced regional economic development.

Efficient Electrochemical Nitrate Reduction to Optimize the Nitrogen Cycle

Xu hui — Mon, 07 Apr 2025 00:00:00 +0800

This study investigates the potential of electrochemical nitrate reduction (ECNR) technology in optimizing the nitrogen cycle, addressing the critical issue of nitrate pollution. By examining various electrode materials, operational parameters, and system designs, the research aims to enhance the efficiency and selectivity of nitrate reduction. Experimental results reveal significant variations in nitrate reduction efficiency among different electrode materials, with platinum demonstrating the highest performance. The study underscores the importance of material selection, electrolyte conditions, and precise control of operational parameters in achieving effective nitrate remediation. The findings contribute valuable insights for both academic research and practical applications, highlighting ECNR’s promise in mitigating nitrate pollution and promoting a healthier nitrogen cycle.

Research on the Path of Integrating the Spirit of Third-Line Construction into Learning-Oriented City Construction-Taking Chengdu as an Example

Sun Zhila, Lai lu — Tue, 08 Apr 2025 00:00:00 +0800

This paper examines the potential of "The Spirit of the Third Line Construction"to contribute to the development of Chengdu as a learning city. The Third-line Construction, a massive industrial relocation effort in the 1960s and 70s, instilled values of self-reliance, innovation, and dedication that continue to resonate in western China. This paper argues that these values can be leveraged to address the challenges facing Chengdu in its quest to become a leading learning city. Through a review of existing literature and analysis of Chengdu’s urban development initiatives, this paper identifies specific paths for integrating the the spirit of the third line construction into learning city construction. The findings provide practical recommendations for policymakers and stakeholders on how to foster innovation, promote lifelong learning, and enhance community engagement in Chengdu.

Analysis of Factors Influencing the Willingness to Accept Carbon Inclusion Market Based on Structural Equation Modeling——Taking Xi'an City as an Example

Yangchen Sun — Wed, 16 Apr 2025 00:00:00 +0800

The purpose of this paper is to investigate the willingness of Xi'an residents to accept carbon benefits, using a combination of principal component analysis and structural equation modeling. First, the data were collected from 16 questions using a five-point scale, and after confirming that the data were suitable for principal component analysis by KMO and Bartlett's test of sphericity, five principal components were extracted, with a cumulative variance explained rate of 87.116%, which realized dimensionality reduction and retained the key information. Second, structural equation modeling was used to construct the model with cognitive situation and decision-making behavior as latent variables. It was found that the perception of the carbon inclusion program's effect on carbon emissions significantly affects the cognitive situation, the greatest impact on satisfaction is whether participants are willing to promote the Carbon for All program, and the use of the carbon inclusion platform by people around us has the greatest impact on the practice situation. This study provides a basis for in-depth understanding of residents' willingness to accept carbon benefits, as well as a reference for the promotion and development of carbon benefits.

Investigation on Tourism Trends Using K-means Clustering and Regression Analysis

Anna Sheila Ilumin Crisostomo, Badar Al Dhuhli, Reggie C. Gustilo — Thu, 08 May 2025 00:00:00 +0800

Purpose: The purpose of this study was to analyze tourism trends by determining the clusters of tourists based on common factors. Three (3) characteristics were explored using k-means clustering namely tourists’ demographics, travel patterns and travel preferences. These clusters were based on individual’s age, gender, country of origin, frequency of travel, travel destinations and seasons. Regression analysis was also performed to determine the factors that influence the length of stay of tourists in their travel destinations.

Methodology: This research conducted a survey from 150 respondents of different age groups, gender, and nationalities. Frequency of travel in a year, length of stay per travel, seasons, destinations, purpose of travel and preferred booking method were the parameters inquired in the survey. The collected dataset was utilized to characterize the clusters of tourists with common considerations. Additionally, regression analysis was used to forecast predictors influencing tourists’ length of stay.

Findings: Three (3) parameters were considered in performing k-means clustering such as tourists’ demographic profiles, travel patterns and preferences. Regression analysis likewise was employed to predict visitors’ length of stay using age, gender, purpose of travel, travel season, and preferred destination as independent variables. In participants’ demographics, number of clusters generated was k=5. Gender and nationalities were found to be randomly clustered while other parameters were categorized according to various age groups and frequency of travel. Consequently, for tourists’ travel patterns, age, gender, country of origin, frequency of stay, purpose of travel, length of stay and travel seasons were used as parameters. The elbow method knee-point revealed (k=6) as the optimal number of clusters. Moreover, travel preferences parameter was also considered for clustering where predictors like gender, age, country of origin, frequency of travel, purpose of travel, travel season and length of stay were utilized. The optimal number of clusters for this category generated K=5. Regression analysis revealed gender, age and purpose of travel as significant factors influencing tourists’ average length of stay. The combination of these variables generated the lowest value of MSE=0.64.

Research limitations/implications: A limited dataset of 150 respondents mainly from Asia and Middle East were utilized in performing preliminary initiatives in analyzing tourism trends. The predictors used in the analysis were restricted to gender, age, country of origin, travel frequency, length of stay, travel season and travel destinations. Supplementary parameters ca be considered in a big data setting for similar studies in the future. K-means clustering was selected among other algorithms with attributes commonality while regression analysis was employed to determine the factors influencing tourists’ length of stay in their destinations.

Social Implications: Results of this study will greatly support individual tourists in determining trends in various travel destinations. Similarly, business owners gain benefit forecasting travellers’ requirements such as accommodation, food, services, etc. Research findings likewise provide informed decisions for stakeholders

Originality / Value: The dataset used were participants from different countries and nationalities which include Philippines, Saudi Arabia, United Arab Emirates, Oman, USA, Portugal, Germany, Malaysia, Thailand, Qatar, Finland, Denmark, Spain Taiwan, South Korea, Singapore, Australia, Austria, England, UK, India and China. The presented codes were programmed in python where analyses and interpretations were based on formulated objectives. K-means clustering, and regression analysis were both employed to present varied clusters according to tourists’ demographic profiles, travel patterns and preferences. Different factors were identified and used to predict tourists’ length of stay in their preferred destinations.