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Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The current relationship between humans and nature is complex and tense. Overexploitation of natural resources, pollution, climate change and biodiversity loss present the main challenges for modern society. In particular, the issue of climate change is being intensely debated, and the interest in protecting natural resources by adopting sustainable practices is growing. Therefore, this chapter examines a brief history and concept of climate change, reviewing relevant theories and authors from Svante Arrhenius and Guy Stewart Callendar to Charles David Keeling and Mikhail Budyko. This chapter explores the first measurements and warnings regarding climate crisis and reviews international treaties and policy development at local, national and global levels. Furthermore, adverse consequences of the climate crisis are described, and ecologism, eco-imperialism and climate change denialism are explained.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

The issues raised in this chapter are primarily those of obfuscation regarding social and economic inequality in the UK. The chapter is about the way discourse in various forms serves to disguise and justify the huge inequalities in this society; legitimising and ‘naturalising’ them, or in Arendt's words ‘lying’ about them so that they are seen as ‘natural and self-evident’ (Alvesson & Deetz, 2006, p. 261). Issues looked at are the institutional arrangements by which government ministers give or withhold resources to and from certain categories of its citizens. This includes the UK Treasury in relation to which economic groups the Chancellor of the Exchequer decides how much to tax or not to tax. In particular what are examined are the discourses justifying these measures and establishing certain ‘truths’ about how things are economically and socially; which categories are entitled to or deserving of certain kinds of resources and which are not – argued here as constituting obfuscations of the ‘actual’ situation. Obfuscation has been defined as the action of making something obscure, unclear, or unintelligible. This, arguably, is not far removed, from the action of being deliberately untruthful or lying. The question then arises as to how close these discourses come to lying and how serious the inequalities are.

This paper draws on the thinking of the nineteenth-century Italian philosopher and poet Giacomo Leopardi and scholars who studied his thoughts on the relationship between nature and humans. Leopardi's philosophy of nature recognizes the alienness of nature in relation to humankind, thus challenging human governance of the planet. The poet’s thoughts align with the dilemma identified in the Anthropocene literature: who speaks for nature? This dilemma has accounting implications in terms of the frameworks and disclosures to be adopted. Therefore, Leopardi’s thoughts can become the basis for a more articulated and complex understanding of some key concepts and issues at the roots of SEA.

The paper utilizes content analysis to examine four essays by Giacomo Leopardi, which serve as the source of our data.

Leopardi recognizes the alienness of nature with respect to humanity and the voicelessness of nature as a generative of conflict. He also warned of the consequences of human governance that does not take nature’s needs into account. These findings open a discussion on the complex accounting implications of the distance between humanity and nature. They can inspire SEA scholars to change the status quo by developing new accounting frameworks from the perspective of nature and adopting forms of governance of nature that recognize the need to protect it as a voiceless stakeholder.

Through Leopardi’s humanistic and poetic philosophy, the perspective of nature can be infused into SEA studies, thereby promoting the need for a multidisciplinary and complex approach to the discipline.