Search results

This paper aims to explore why a country with significant under-investment in water infrastructure has not successfully imposed domestic water charges. Drawing on an economization lens, it examines how an economy emerged in the imposition of water charges but was subsequently hidden due to their politically motivated suspension.

Drawing on documentary evidence, a theoretically informed examination of the “economization” process is set out. This examination recognizes the central role sustainability plays in water management but illustrates how sustainability must be integrated with environmental, social, economic, cultural and political factors.

The findings set out the challenges experienced by a state-owned water company as they attempt to manage domestic water charges. The paper reveals that while the suspension of water charges has hidden the “economy” within government subvention, the economic and sustainable imperative to invest in and pay for water remains, but is enveloped within a political “hot potato” bringing about a quasi-political/quasi-economic landscape.

The findings demonstrate how the effective and sustainable management of domestic water supply requires collaboration between multiple participants, including the government, the European Union, private citizens and the water protest movement.

While highlighting the challenges faced by a country that has seriously under-invested in its water resources, the paper reflects the societal consequences of charging individuals for water, raising important questions about what water actually is – a right, a product or a political object.

Showing how an economy around domestic water supply in Ireland was revealed, but subsequently hidden in “the political”, the paper illustrates how sustainability is as much about economics and politics as it is about ecological balance and natural resources.

The purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework.

This study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022.

Decriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for.

New to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds.

Public health measures implemented early in the COVID-19 pandemic brought the idea of essential work into the public discourse, as the public reflected upon what types of work are essential for society to function, who performs that work, and how the labour of essential workers is rewarded. This chapter focusses on the rewards associated with essential work. The authors develop an intersectional lens on work that was officially deemed essential in 2020 to highlight longstanding patterns of devaluation among essential workers, including those undergirded by systemic racism in employment and labour law. The authors use quantitative data from the CPS-MORG to examine earnings differences between essential and non-essential workers and investigate whether the essential worker wage gap changed from month to month in 2020. The authors find that patterns of valuation among essential workers cannot be explained by human capital or other standard labour market characteristics. Rather, intersectional wage inequalities in 2020 reflect historical patterns that are highly durable and did not abate in the first year of the global pandemic.

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.

As the tourism industry recovers from the devastating effects of the global pandemic, meeting the targets of the Sustainable Development Goals (SDGs) remains to be a global “deadline” where tourism is seen as a major contributor. While disruptions to business-as-usual practices such as COVID-19 present unprecedented challenges, they can also provide opportunities for strategic innovation to change behavior toward sustainable tourism experiences. Active transport for low-carbon tourism such as walking or cycling tours have risen in popularity in recent years, and especially postpandemic, as they provide opportunities for a more personalized experience while health and safety protocols can still be implemented. They also present health benefits for the individuals while contributing to environmental sustainability and climate mitigation strategies of the tourism industry. This book chapter presents cases of various forms of tourism activities that use active transport, focusing on walking tours and cycling tours. Various companies offering tours under these modes are discussed and presented. These two modes promote authentic cultural and heritage tourism experiences through the local experts that provide the services.

This chapter explores how new tourism policy paradigms can emerge and settle in a wicked-problems scenario characterised by high labour informality. Acknowledging the growing importance of the tourism sector in Argentina, where labour informality has long been a concern, the authors focus on an ambitious and unprecedented tourism policy: PreViaje. Established in 2020, PreViaje is a program that promotes the selling of tourism services in advance to residents travelling within Argentina. It is designed around incentives to encourage formality via both tourism supply and demand. After looking at the outcomes of PreViaje, relevant matters to consider for future program editions are identified. These relate to temporal and spatial dispersal concerns, and trade-offs regarding economic, social and environmental matters.

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.