The potential for carbon reduction in construction waste sorting: A dynamic simulation

In the face of rampant urbanization, industrialization, and continuous advancements in construction technology, sustainable development in the construction industry becomes increasingly imperative. A promising avenue toward this sustainability is through the adoption of Recycled Construction Materials (RCMs), yet their widespread use remains complex and filled with numerous barriers, signifying an urgent need for the systematic investigation of these obstacles. This study uniquely employs a qualitative PEST (Political, Economic, Social, and Technological) analysis to illuminate the intricate impediments to the adoption and promotion of RCMs. Data for this study were collected through in-depth, semi-structured interviews with a variety of experts in the field. The research identifies substantial barriers within each PEST category. Political factors include regulatory biases and limited funding for RCM research, while economic factors involve the higher costs and limited availability of RCMs. Social aspects revolve around public awareness, safety concerns, and resistance to change within the industry. Technological issues focus on the development, performance, and compatibility of RCMs, the slow innovation pace, and the absence of standardized guidelines. Additionally, this study stands out by suggesting strategic, context-specific recommendations aimed at surmounting these obstacles and further fostering the adoption of RCMs. The solutions proposed are intimately linked to the challenges identified, highlighting the practical value and relevance of this study for guiding future research and policy development in the face of ongoing advancements in construction technology.

Overcoming Barriers to the Adoption of Recycled Construction Materials: A Comprehensive PEST Analysis and Tailored Strategies

The rapid growth of the construction industry has led to significant environmental issues, notably the sharp escalation in construction waste. Recent endeavors have prioritized on recycling and reusing of construction waste. Nevertheless, despite the evident public support, the adoption of recycled products remains constrained, primarily as a result of subsequent unfavorable implicit perceptions. To understand the end users' implicit attitudes, this study employed a Single-Category Implicit Association Test (SC-IAT) and functional near-infrared spectroscopy (fNIRS) for analysis. The experiment results confirm the existence of detrimental implicit attitudes, even as self-reported attitudes remained positive. The experiment results, D-scores, suggest that emotional interventions have stronger impact as they reach peak (15.9s) 36% faster that the rational interventions. The proposed implicit attitude measurement approach can be extended to understand and promote other green products and recycled materials. 

Interfering Implicit Attitudes of Adopting Recycled Products from Construction Wastes

The public' s disfavour of recycled aggregate (RA) produced from construction and demolition waste is recognized as a main hinder for RA's promotion. To understand their real thought, this study measures not only their explicit attitude but also implicit attitude. Particularly, this study explores the effective strategy of information intervene to change their attitude based on psychological experimental methods. The initial measurements show that subjects prevailingly report positive explicit attitude, but indeed, majority of them hold negative implicit attitude. The information intervention can positively change both explicit and implicit attitude. Large quantity of rational information and small quantity of affective information are more effective to change explicit attitude. Whereas, large quantity of rational information is the most effective way to change implicit attitude. The supposed ironic effect does not appear. This study provides valuable psychological guidance for the publicity campaign of RA and the development of circular economy in the construction industry. 

Revealing the implicit and explicit attitudes of the public towards recycled aggregate based on psychological experiment

In China, the annual generation of construction and demolition waste (CDW) has been steadily increasing, accompanied by a generally low recycling rate. To promote sustainable development, there is an urgent need to enhance the recycling of CDW. This paper aims to investigate the determinants of construction stakeholders’ intention to use CDW recycling products in China. The stimulus–organism–response (S-O-R) model, integrating the technological–organizational–environmental (TOE) framework, personal perceptions, personal traits, and the intention to use, was chosen as our theoretical model. Through an analysis of 272 valid questionnaires, the partial least squares structural equation modeling (PLS-SEM) was utilized to evaluate the model and test the proposed hypotheses. The results indicated that personal traits are the most influential factor shaping construction stakeholders’ intention to use, followed by personal perceptions, while external stimuli exert no direct significant impact on the intention to use. Nevertheless, personal traits and personal perceptions play a significant mediating role in the relationship between external stimuli and the intention to use, forming a noteworthy serial chain mediation. The research findings imply that in China, bolstering personal traits plays a critical role in guiding and promoting the intention to use CDW recycling products. 

Investigating the Determinants of Construction Stakeholders’ Intention to Use Construction and Demolition Waste Recycling Products Based on the S-O-R Model in China

Investigating the implicit stereotypes of recycled construction and demolition waste products with an fNIRS-enabled single category implicit association test

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiayu Chen

Author Tools

Chat about Author

Papers

Investigating the implicit stereotypes of recycled construction and demolition waste products with an fNIRS-enabled single category implicit association test