The Impact of Construction Labour Productivity on the Renovation Wave

Abstract

The European Green Deal's Renovation Wave aims to renovate 35 million energy-inefficient buildings to reduce carbon dioxide (CO2) emissions by at least 55% by 2030. Historically, efforts to reduce CO2 emissions focused on Operational Energy (OE) of the finished buildings. However, in recent years the Embodied Energy (EE) of the building’s construction process has gained attention because of its essential role in construction renovations projects. In this context, construction efficiency, and more precisely, workers’ efficiency, is a vital catalyst to achieve the European Union (EU) targets. To identify the impact of Construction Labour Productivity (CLP) on the renovation wave an exploratory case study was adopted as a research strategy. Data from four domestic housing renovation projects were gathered. Three specific research goals are outlined. The first is to demonstrate the effect of the adoption of Lean tools and methods to increase CLP. The second is to quantify the correlation between improved productivity and the EE emissions saved during the construction phase. The third goal is to estimate the effect the higher productivity has on OE emissions. The results show that the adoption of several Lean tools and methods has a potential to improve CLP to 45%. This rate of improvement for the 35 million housing units to be renovated could save 6.9 million tonnes CO2e from EE and 386 million tonnes CO2e from OE. This novelty link between process improvements and reduced energy consumption and emissions can support politicians and infrastructural developers in decision-making for a more sustainable construction industry.

Innovation opportunities from made-to-order gypsum boards in renovation projects: a case study in Denmark

“…LC aims at removing waste, increasing value, reducing costs, and improving the overall quality of products and processes [19]. Wandahl et al [7] estimate that applying LC tools and methods can reduce waste and energy consumption in construction processes and thereby save 6.9 million tonnes CO2eq. from embodied emissions in Denmark (if applied to 35 million housing units).…”

“…WS studies generally adopt the Ohno's [25] understanding of work as divided into Value-Added Work (VAW), understood as time spent in Direct Work (DW); and Non-Value-Added-Work (NVAW), understood as the time spent in Indirect Work (IW) and Waste Work (WW). Previous research [7] generally monitored construction operations using six-work activities categories. Two categories of DW: production and talking; two categories of IW: preparation; and transportation.…”

“…In recent years, the materials and processes applied during the renovation phase have received increasing attention to reduce embodied emissions [2][3][4][5][6]. Next to environmental considerations, cost and time efficiency is a key consideration in the management of renovation projects [7] as for example promoted by the EU green deal that emphasises the importance of highly efficient construction processes to achieve as much green transition in the built environment as possible [8]. In Denmark, there is a strong policy focus on the renovation of existing buildings in the most costefficient way [9,10].…”

Automating the Estimation of Productivity Metrics for Construction Workers Using Deep Learning and Kinematics

“…In general, construction labor productivity (CLP) can be defined as the product output per person-hour worked. Several other factors are relevant when examining productivity, but as a measure of performance, CLP has been used alone as it is a significant factor for the total project cost, as well as because it is the only factor that is conscious of its contribution (Wandahl et al, 2021). This paper (1) gives an overview of current activity recognition and productivity estimation research in construction, (2) introduces a deep learning method for productivity estimation, (3) shows the early implementation and preliminary results of the model, and (4) discusses the future work regarding the method, the current challenges, and the potential.…”

“…CLP has been extensively studied using manual methods, especially work sampling (Araujo et al, 2020;Wandahl et al, 2021;Gouett et al, 2011). Work sampling can be used to estimate CLP due to the direct correlation between the direct work (DW) category "producing" and productivity.…”