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Development of geothermal power plants and local geothermal energy initiatives have lagged due to the social problems such as conflicts with stakeholders such as Onsen (Hot Spa) owners, despite the abundant geothermal resources. Study area was Tsuchiyu Onsen in Fukushima prefecture, Tohoku (Northeast) District, Japan, where the Great East Japan Earthquake and Fukushima Nuclear Accident occurred in 2011, and the reconstruction and local initiatives of geothermal energy were still unclear. Agent-based modeling (ABM) is an effective methodology for modeling and analysis of opinion formation. Parameter estimation method was proposed to extract appropriate parameters from various factors through a Bayesian Network. The characteristics of stakeholders and communities that affected opinion formation in the survey area were successfully extracted. Here we show the sufficient methodology to quantify the characteristics of each person using survey data, and to extract the parameters of the agent by data-driven inverse analysis. By using this methodology, we could reproduce opinion diversity, which is a property of opinion formation in real communities. This result suggests that the model replicates the actual formation of opinion in Tsuchiyu, where the economy was boosted by the construction of a binary cycle power plant.

Development of geothermal power plants and local geothermal energy initiatives have lagged due to the social problems such as conflicts with stakeholders such as Onsen (Hot Spa) owners, despite the abundant geothermal resources. Study area was Tsuchiyu Onsen in Fukushima prefecture, Tohoku (Northeast) District, Japan, where the Great East Japan Earthquake and Fukushima Nuclear Accident occurred in 2011, and the reconstruction and local initiatives of geothermal energy were still unclear. Agent-based modeling is an effective methodology for modeling and analysis of opinion formation. Parameter estimation method was proposed to extract appropriate parameters from various factors through a Bayesian Network. The characteristics of stakeholders and communities that affected opinion formation in the survey area were successfully extracted. Here we show the sufficient methodology to quantify the characteristics of each person using survey data, and to extract the parameters of the agent by data-driven inverse analysis. By using this methodology, we could reproduce opinion diversity, which is a property of opinion formation in real communities. This result suggests that the model replicates the actual formation of opinion in Tsuchiyu, where the economy was boosted by the construction of a binary cycle power plant.

Having tools and techniques of project management are a necessary but not a sufficient condition for project success. If a manager cannot handle people, she or he will have difficulty with managing projects. Many successful projects have teams of people that are involved and committed; however, friction between the team members still occurs due to misunderstandings, conflicts, and personality differences. Project managers must be prepared to deal with these behavioral problems of team members. One way to minimize the impact of behavioral problems is to provide training for all team members (including managers themselves) in interpersonal skills. This area has often been neglected in many organizations; other times, just managers are required to be trained but not team members. This paper presents a unique opportunity for both managers and team members to gain insight into understanding the complex nature of collective behavior. The only way to understand how individual behavioral problems translate into those of a team is to model problems using a bottom-up simulation method like the one presented here. This introductory paper presents basic concepts and a simple application of Agent-based modeling.

Dealing with the social and political impacts of large complex projects requires monitoring and responding to concerns from an ever-evolving network of stakeholders. This paper describes the use of text analysis algorithms to identify stakeholders&rsquo, concerns across the project life cycle. The social license (SL) concept has been used to monitor the level of social acceptance of a project. That acceptance can be assessed from the texts produced by stakeholders on sources ranging from social media to personal interviews. The same texts also contain information on the substance of stakeholders&rsquo, concerns. Until recently, extracting that information necessitated manual coding by humans, which is a method that takes too long to be useful in time-sensitive projects. Using natural language processing algorithms, we designed a program that assesses the SL level and identifies stakeholders&rsquo, concerns in a few hours. To validate the program, we compared it to human coding of interview texts from a Bolivian mining project from 2009 to 2018. The program&rsquo, s estimation of the annual average SL was significantly correlated with rating scale measures. The topics of concern identified by the program matched the most mentioned categories defined by human coders and identified the same temporal trends.

Renewable energy has come to hold a significant place in public discourse surrounding the future supply of energy worldwide. Defined by their ability to replenish themselves within a reasonable time frame, renewable energy resources are extremely varied, as are the technologies that are used to harness them. Renewable energy encompasses a wide range of fields and disciplines, from physics, chemistry, geology, and engineering to manufacturing, electricity production, and economics. This chapter discusses several key renewable energy resources and technologies, including solar, wind, biomass, geothermal, and hydropower. It provides a simple technical overview of the resources and the mechanisms behind their respective technologies, along with a discussion of the benefits and challenges associated with each resource and technology covered. Finally, it includes a discussion of the state of the industries to help policy makers and political scientists understand the current status—and the potential for future developments—of each area.

Purpose This paper explains how recent statements by the US Securities and Exchange Commission’s (SEC’s) leadership – including the new Chief of the SEC’s Foreign Corrupt Practices Act (FCPA) Unit – signal the American regulator’s intent to “level the playing field” by stepping up its investigations and enforcement of companies worldwide and what non-US issuers can do to prepare. Design/methodology/approach Uses information included in the announcement naming Charles E. Cain as Chief of the SEC’s specialized FCPA Unit to lay out an argument that the Unit’s priorities may focus more on non-US companies than US companies. Findings Based on past statements and written accounts made by Mr Cain, and with the tacit support of other senior SEC officials, it can be assumed that non-US companies will experience additional scrutiny from the SEC, in the name of leveling the playing field. Furthermore, it can be assumed that the SEC will place additional pressure on anti-corruption regimes in other international jurisdictions to do their part in combatting corruption. Originality/value This paper is of value to personnel within non-US issuers who are responsible for creating and enforcing their organization’s anti-bribery or anti-corruption policies and internal controls. It is also of value to legal counsel interested in developing an understanding of the current priorities of the SEC as far as the FCPA is concerned.

Interest in and understanding of the various competencies that university sustainability and environmental graduate degree programs should aim for has increased in recent years. Yet empirical efforts that assess the effectiveness of programs from a competency building perspective have visibly lacked—particularly from a macro-perspective examining multiple cases. This study fills this gap in the literature by conducting a novel comparative assessment of different types of master's degrees in a sample of 14 programs from top-performing universities in Europe, Asia and North America. Our study uses quantitative and qualitative approaches to assess the effectiveness of differing types of programs at building key sustainability competencies defined in the literature, and to understand the defining characteristics of programs, innovative competency building approaches, challenges encountered, and potential countermeasures. Using a typological methodology, this study classifies programs into three categories: research-oriented, neutrally-oriented and practice-oriented and then examined the competency building effectiveness of each category through questionnaires administered to faculty (n = 40) and students (n = 205). Results revealed low success in all program types at equipping graduates with anticipatory competencies. Statistically significant differences were also observed between research-oriented and practice-oriented programs, with the latter demonstrating higher success in building interpersonal, strategic and normative competencies. Qualitative questionnaire responses revealed a widespread student demand for more practice-orientated learning and collaborative projects with societal stakeholders. However they also highlight the important role of research-oriented programs at equipping students with theoretical and conceptual knowledge. From a sustainability competency building perspective, the findings point to a need for research-oriented programs to integrate practice-based didactic approaches for building skills and methods via real-world learning projects with external stakeholders. They also prompt a reconsideration of the special importance of research-oriented sustainability and environmental degree programs in a higher education landscape increasingly shaped by vocational and job market expectations.

The social license to operate is an emergent convergence of opinion, the production and durability of which is highly interdependent with the structure of the stakeholder network. This structure evolves over time as new links are formed and old links decay. In this paper, we propose a social license model in which agent interactions lead to the evolution of network structures, which are then classified according to observed stakeholder networks.

This is a companion paper to an earlier work in which an agent-based model is proposed by Nakagawa et al. for exploring the emergent phenomena of social license to operate (SLO) of a mining company. In this paper, the structure of the original model is described, along with the enhanced ability for the two-way diffusion of information and opinion among agents. This is achieved through the addition of a global “dialogue” variable, which dictates the extent to which higher influence agents accept opinion from agents of lower influence. Initial findings suggest that the bidirectional diffusion of information has a large effect on the time that the modelling population takes to reach a Social License consensus, and the effect is especially pronounced for low dialogue values. In other words, the Social License of communities characterized by a low preference for dialogue (as opposed to “top-down” mandated communication) will be largely affected by small changes in the preference for dialogue. Findings also suggest that as a modelling community becomes more and more open to dialogue, the effect on the time to consensus becomes less and less pronounced until it becomes negligible at a fairly low dialogue level.

We attempt to understand, scientifically, how different members of the mining concession, impacted communities, and government authorities behave when a conflicting situation arises. The main purpose of our effort is to start developing a framework for the scientific modeling of stakeholders’ behavior, and we create a reality-driven generic scenario of conflict. We assume that the managers and superintendants of a mining operation currently envision a problem; one that tests the limits of the commitment of the company’s mission statement, and of the spectrum of actions taken which are embedded in the “culture” of the company’s corporate social responsibility. It is an “event” that highlights the nature of an overall problem that the company would like to predict and act proactively: the integration of scientific tools, sustainability, and cultural realities within a mining framework. We adapt an agent-based modeling approach and start with a theoretical understanding of certain social behavior, build a model, and simulate “what if” scenarios to understand its dynamics to gain a better insight of the complexity of a seemingly simple social system of interest.