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Early studies projected potential societal, economic and environmental benefits by the widespread deployment of Autonomous and Connected Transport (ACT) promising a significant reduction of transport costs and improvement in road safety. An effective way of assessing ACT impact is via simulations, where results are largely affected by the scenarios defining the ACT development. However, modelled scenarios are very diverse due to the huge uncertainty in ACT development and deployment. This chapter aims to shed light on the different ACT simulation scenarios and sustainability aspects that should be considered while developing or reporting the simulation results. To this end, this chapter discusses the various simulation approaches, what the required (or the typically utilised) pipelines are, and how some components are more important or less important than in ‘classic’ modelling and simulation approaches. Special focus is dedicated to the uncertainty related to ACT operational parameters and how these will impact transport modelling. To address said uncertainty, an analysis of current approaches to scenario building is provided, as the chapter guides the reader through different methodologies and clusters them in relation to the desired indicators. Finally, the chapter identifies and proposes Key Performance Indicators (KPIs) that are useful when applying simulation tools to assess ACT scenarios. These KPIs can be used for simulation scenario development to test particular sustainability aspects of ACT deployment and relevant policies.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

Serious games (SGs) are virtual systems that allow the reconstruction of the laws governing the behavior of complex adaptive systems such as urban transportation and social interaction. Unlike stated preference-based studies, improved visualization, feedback, and scores mediate players’ learning through experience. SG’s potential to understand users’ preferences regarding shared automated vehicles (SAVs) is developed. The investigation focused on three innovative, entirely automated commuting options: shared rides, shared cars, and automated transit. The research involved 10 participants actively involved in a competitive mode selection exercise, which emulated 50 workdays and was conducted in 10 separate sessions. The players aimed to maximize their overall score influenced by their mode choice, punctuality, and the other players’ choices. SG-obtained data was used to estimate a game-based discrete choice model. The sustainability policy implications of game-based methods on the future adoption of SAVs and impacts on other modes are further discussed.

The study is called for to eliminate the noise between the significant macro variables from the perspective of the cause-and-effect approach to indicate why and how the return of solar projects is being affected by these.

The study aims to investigate the spread between unit selling electricity prices of a monthly production of 250 KW solar project installed in Türkiye and USD/TRY.

A relational framework is designed by drawing on the variables determined as crude oil prices, United States (US) 2-year yield, Dollar Index (DXY), USD/TRY, the annual inflation rate of Türkiye, and unit selling electricity prices. Then, a multivariate approach is performed through Matlab to analyse the correlational relationships and structure the curve estimation models.

The observations show that the gradually rising spread between unit selling electricity price and USD/TRY signals the reduction in return-on-investment rate of solar energy projects because of the particular causes of the European energy crisis by the reason of Russia and Ukraine war and escalating risks in DXY and US treasury yields as a result of federal fund rate hikes against inflationary pressures. Solar energy investments are delicate instruments to global oil shocks and higher DXY in controlling Inflation and currency volatility; therefore, resilient policies should solicit the demand because of environmental and economic reasons to reduce the external dependency of Türkiye.

In this chapter, the authors evaluated the financial security of “Kvarts” Joint-Stock Company (JSC) and “Kattakurgan oil” JSC operating in Uzbekistan, considering external factors. The authors tested two main hypotheses. According to the first hypothesis, the statistical panel model, which considers fixed effects and random effects in the cross section, is estimated using the least squares method of the cumulative model. Regression analysis is used in the panel data model. It is desirable to perform the calculation using the method of least squares in this model as well. According to the alternative hypothesis, it is a dynamic panel model. This model studies the dependence of the independent variable in the previous period. If there is an autocorrelation of the residual between the predicted independent variable and the actual independent variable in the regression line of the statistical panel model, then the dynamic panel model is used by the lagged independent variable. Based on the official reports of “Kvarts” JSC and “Kattakurgon oil” JSC, the authors created panel regression model indicators based on financial security indicators using the Eviews-9.0 program. Moreover, the authors developed forecast indicators of their financial security status indicators until 2025.

Corporate social responsibility (CSR) promotes society, reduces risk, and encourages ethical business practices. Due to its relevance, we study how CSR influences firms' sustainable development. We analyze data from 427 New York Stock Exchange (NYSE)-listed firms from 2008 to 2022. The Refinitiv environmental and social score is used to measure CSR, whereas for firms' sustainable development we rely on corporate sustainable growth rate (SGR) and market-based metrics. The analysis employs various econometric techniques, including ordinary least square, fixed effect regression, two-stage least square, generalized method of moment, and simultaneous quantile regression. The results indicate that CSR has a positive and significant effect on firms' sustainable development across all models. This relationship supports the notion that socially responsible business can contribute to long-term financial sustainability in line with “stakeholder theory”, indicating that companies should accommodate the concerns of various stakeholders, including society and the environment, to achieve sustainable development. We evaluate how the conditional distributions of SGR and firms’ value are affected by CSR, categorizing them into high, moderate, and low regimes. The quantile regression estimates indicate that the effect of CSR is more pronounced at upper quantiles, followed by moderate and low regimes. These findings underscore the importance of considering CSR in assessing the SGR and enterprises market value. We also confirm that our results are robust under range of different econometrics' methods. Finally, we enlighten current literature, and our research has useful policy implications for management and investors.

This paper explores sustainability within supply chain management and its link towards resilience through the lens of ethics. The wicked problem of sustainability impacts supply chains and society at large, and the ability of supply chains to remain viable in the future is based on the sustainability of business practices. The paper argues that a substantive proportion of sustainability mechanisms (such as the Triple Bottom Line approach and codes of conducts) can be viewed within the ethical paradigm of deontology, whereby the morality of an action is based on adherence to rules. However, there are numerous critiques of such approaches and their success in creating more sustainable practices. Therefore, the paper proposes there is a need for systems thinking approaches to be incorporated into the exploration of the link between ethics, sustainability and supply chain resilience. Using two examples of social sustainability issues in Australia – modern slavery legislation and horticultural ¹ worker exploitation – the paper demonstrates the utility of various systems thinking methodologies to explore the complexity of these issues. The paper sets out a conceptual call to arms for researchers and practitioners to apply a holistic lens towards how the morality of actions is shaped and influences supply chain sustainability practices.

Local governments use taxes on future increases in property values to pay for current economic development through tax incremental financing (TIF). TIF is a powerful tax tool used to spur improvements to a designated area. Proponents of TIF argue that it allows local governments to make investments without affecting previously established government and school district programs. Detractors argue that because the TIF designation denies existing overlapping districts (e.g., schools) the benefits of increases in property values, TIF can have a negative impact on a community. Empirical evidence on the economic and fiscal effects of TIF is mixed. This paper describes the potential costs and benefits associated with the use of TIF and then summarizes prior research on outcomes associated with this widely used property tax program.