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In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.

The purpose of this paper is to quantify the benefits to farmers from using alternative marketing arrangements (AMAs) in the USA. The authors first estimate a behavioral model explaining farmers' joint decisions on which commodities to produce and which marketing channels to use when selling their outputs. The authors then use the estimated model to quantify the benefits to farmers from using AMAs.

The authors use the discrete choice random utility maximization model to examine farmers' choices on production regimes, where a regime is defined as a possible combination of all the individual commodity/marketing arrangement channels that the farmer can choose to use. The farmer is assumed to compare the utilities he gets from each of the possible production regimes and then selects the production regime that yields the highest utility to him. The benefit of having access to a particular AMA is measured as the negative of the welfare loss associated with forcing the farmer to abandon that particular AMA.

The results indicate that AMAs yield an economically significant amount of benefits to farmers who rely on them to market their outputs. At the national level, the benefit of using production contracts to hog farmers is valued at $336.4 million. The benefits of using marketing contracts are valued at $374.2, $156.6 and $92.1 million for corn, soybeans and wheat producers.

The paper is the first study that uses the farm-level data to study the welfare effects of marketing contracts in the grain sector. The results show that considering a multi-enterprises farm, farmers' welfare loss might be smaller when the hog production contract is no longer existed.

This paper aims to report on homebuyers’ preferences and willingness to pay for installed home photovoltaic systems. Their influence on the market position of a dwelling is relatively unknown. Considering that expected lifespan of photovoltaic systems is at least 25 years, it is likely that many dwellings with a photovoltaic system will enter the housing market.

Few houses with installed photovoltaic systems have been sold in the market to date. Lack of real market data imposes a method based on the stated preference data. Therefore, the general preferences toward photovoltaic systems are determined by a discrete choice model based on responses of 227 homebuyers in the Eindhoven region, The Netherlands. Further, the model estimates were used to assess the indirect willingness to pay for home photovoltaic systems. This initial willingness to pay is further reassessed with the direct willingness to pay collected in an open-ended questionnaire format.

Results of the model show that the homebuyers’ preferences for home photovoltaic systems are large and significant. In addition to general preferences, this article reports on the taste heterogeneity carried out by separating observations based on the respondents’ characteristics. For example, photovoltaic systems are more appealing to homebuyers in more urban or central neighbourhoods. Further, the results of the direct survey lead to the conclusion that people are probably willing to pay close to the replacement value of the system and only 22 per cent of all respondents did not want to pay anything for the installed photovoltaic system.

These findings are exploratory and they raise a number of questions for further investigations, such as those regarding the real estate value of the installed photovoltaic systems. The reported findings must be regarded as local, thus further research is necessary to understand the impact on European housing markets.

Preferences and willingness to pay for home photovoltaic systems can provide a variety of economic, social and political recommendations to different interested parties such as homeowners, buyers, realtors, retailers, energy companies and governments. For instance, a homeowner would like to know what would be the effect of a photovoltaic system on the housing market.

As per the knowledge of authors, this is the first paper to estimate the impact of an installed photovoltaic system on housing choice, measured by stated choice data in the local housing market. It expands the existing body of knowledge for increasingly important issues of valuing and measuring preferences for photovoltaic systems installed on dwellings.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

This case study draws on interviews conducted with officials from the Rhode Island Department of Environmental Management (DEM), City of Woonsocket and Town of North Smithfield. Additionally, it pulls from relevant legal documents, recordings and minutes from meetings of the Woonsocket City Council and North Smithfield Town Council, City Council resolutions, state legislation and local press coverage.

From 2012–2017, the communities of Woonsocket and North Smithfield engaged in a protracted dispute concerning wastewater disposal. For 30 years, the two jurisdictions had maintained a signed service agreement. Following its expiration; however, Woonsocket imposed a new host fee on North Smithfield. Woonsocket needed to upgrade the facility to comply with mandates from the RI DEM. Over the next five years, leaders from both jurisdictions vociferously fought over the new fee. At the same time, leaders within communities experienced their own divisions. This case study highlights the challenges that decision-makers faced in both communities.

This case is appropriate for graduate and executive level courses in environmental policy, communication and leadership.