Firms operating in environmentally vulnerable contexts will inevitably face difficult cases where long-term profits clash with environmental values. This remains true even with enlightened management and strong regulation. Yet, business models fail to acknowledge this inconvenient truth. This chapter explains why current business models reach an "outer boundary" in their ability to incorporate intrinsic values such as environmental integrity. It introduces two key concepts: the efficiency model and the value gap. Efficiency models use terminology designed either to optimize the allocation of scarce resources toward measurable future goals or to explain the optimal achievement of past goals. A value gap arises when ideal social corporate action diverges from ideally efficient corporate action. The presence of large, recurring value gaps in extractive industries signals the need for fundamental changes – both in corporate decision-making and in the business models that shape it.

Today many have predicted the death of environmental, social, and governance (ESG). Alas, even amidst such predictions, there remains considerable confusion about ESG’s meaning. Some view ESG as synonymous with corporate social responsibility or stakeholderism, others view ESG as a mechanism for assessing risks; some characterize ESG as political, others view ESG as inextricably aligned with business goals. The lack of consensus around ESG’s meaning makes assessing its demise complex. On the one hand, any future version of ESG will be incompatible with alternative – and strongly held – conceptions of ESG, confirming predictions of ESG’s demise while ensuring that ESG’s future will be plagued by controversy and discontent. Nonetheless, there is a version of ESG that is both sustainable because it focuses on economic risks and opportunities, and also beneficial because it may move the needle on improving shareholder value while positively impacting critical social issues.

This chapter introduces unsupervised learning, where algorithms analyze data without predefined labels or target outcomes. It covers three main clustering approaches: agglomerative clustering (bottom-up approach merging similar data points) and divisive clustering (top-down approach, exemplified by k-means algorithm that partitions data into k groups by minimizing distances to centroids).

The chapter explains Expectation Maximization (EM) algorithm for handling incomplete data and finding maximum likelihood parameters in statistical models. It includes a section on reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments to maximize rewards.

Key topics include distance matrices, dendrograms, cluster evaluation metrics (AIC, BIC), and practical applications. The chapter emphasizes the artistic nature of unsupervised learning, requiring careful design decisions about thresholds, cluster numbers, and technique selection. Hands-on R examples demonstrate each method using real datasets.

Until the COVID-19 pandemic, a working style in Japan that emphasized teamwork was predominant, and telework was not widespread. However, due to the COVID-19 pandemic, a majority of companies had no choice but to introduce telework. Telework, where individual workers operate independently rather than collectively, was an entirely new way of working for many Japanese individuals. To make telework function efficiently, a re-evaluation of Japan's traditional employment system, where job descriptions are not specified in employment contracts and individuals agree only to become members of a company, became necessary. While it was previously considered an obligation for workers to comply with employers’ transfer orders involving relocation, telework has introduced a new option of handling such orders without physically relocating. In this way, telework has the potential to be a game-changer in Japan's traditional employment system. However, there are diverse legal issues that need to be resolved when introducing telework.

This chapter explores processes of legalisation in the context of multi-stakeholder initiatives (MSIs) in extractive industries. It analyses three major regimes: the Kimberley Process Certification Scheme (KPCS), launched in 2002 to curb the trade in conflict diamonds; the Extractive Industries Transparency Initiative (EITI), a global standard promoting accountability in natural resource governance; and the Voluntary Principles on Security and Human Rights (VPs), adopted in 2000 to address abuses by private security forces protecting corporate assets. These initiatives display a distinctive focus on implementation. Participants may receive substantial guidance and support to assist with compliance. State and non-state actors contribute to the production of guidelines, toolkits and performance indicators designed to assist corporations in rule implementation on the ground and in specific contexts. When violations occur, members may support the offending party in implementing reforms before resorting to sanctions.

This book began as an empirical study of MNCs’ tax avoidance strategies but evolved into a broader exploration of jurisdictional arbitrage as a distinct modality of corporate power. While conventional accounts treat arbitrage as a technical tool for tax minimization, this study reframes it as a systemic outcome of the CCMCE – a modular structure uniquely suited to navigating and exploiting global regulatory fragmentation. Jurisdictional arbitrage is not confined to taxation; it spans liability shielding, disclosure manipulation, and intangible asset transfers. In so doing, it reconfigures firm–state relations and blurs legal boundaries, allowing MNCs to opt out of regulatory constraints without direct confrontation. This form of power – adaptive, evasive, and anticipatory – is distinct from traditional accounts of lobbying or capture. It operates in the interstices of legal regimes, producing a ‘rule-based transgressor elite’ that consolidates wealth and influence across borders. Jurisdictional arbitrage, the book argues, must be understood not as legal deviance, but as an institutionalized response to contradictions in global capitalism. The book calls for a rethinking of core theoretical assumptions in political economy, especially those concerning sovereignty, regulation, and corporate personhood in an era defined by intangibles and fragmented authority.

This chapter explores the fundamentals of data in data science, covering data types (structured vs. unstructured), collection sources (open data, social media APIs, multimodal data, synthetic data), and storage formats (CSV, TSV, XML, RSS, JSON). It emphasizes the critical importance of data pre-processing, including data cleaning (handling missing values, smoothing noisy data, data munging), integration, transformation, reduction, and discretization. Through hands-on examples, the chapter demonstrates how to systematically prepare "dirty" real-world data for analysis by addressing inconsistencies, outliers, and missing information. The chapter highlights that data preparation is often half the battle in data science, requiring both technical skills and careful attention to data quality and bias.