Search results

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

Lean construction (LC) consists of very effective techniques; however, its implementation varies considerably from one industry to another. Although numerous lean initiatives do exist in the construction industry, the research topic related to LC implementation is still unexplored due to the scarcity of validated assessment frameworks. This study aims to provide the first attempt in developing a structural model for successful LC implementation.

This study developed a Lean construction model (LCM) by critically reviewing seven previous LC frameworks from different countries, defining 18 subprinciples grouped into 6 major principles and formulating testable hypotheses. The questionnaire was pre-tested with 12 construction management experts and revised by 4 specialized academics. A pilot study with 20 construction units enhanced content reliability. Data from 307 Moroccan construction companies were collected to develop a measurement model. SPSS V. 26 was used for Exploratory Factor Analysis, followed by confirmatory factor analysis using AMOS version 23. Finally, a structural equation model statistically assessed each construct's contribution to the success of LC implementation.

This work led to the development of an original LCM based on valid and reliable LC constructs, consisting of 18 measurement items grouped into 6 LC principles: Process Transparency, People involvement, Waste elimination, Planning and Continuous improvement, Client Focus and Material/information flow and pull. According to the structural model, LC implementation success is positively influenced by Planning and Scheduling/continuous improvement (β = 0.930), followed by Elimination of waste (β = 0.896). Process transparency ranks third (β = 0.858). The study demonstrates that all these factors are mutually complementary, highlighting a positive relationship between LC implementation success and the holistic application of all LC principles.

To the best of the authors’ knowledge, this study is the first attempt to develop a statistically proven model of LC based on structural equation modelling analysis, which is promising for stimulating construction practitioners and researchers for more empirical studies in different countries to obtain a more accurate reflection of LC implementation. Moreover, the paper proposes recommendations to help policymakers, academics and practitioners anticipate the key success drivers for more successful LC implementation.