The incorporation of environmental, social, and governance (ESG) criteria into corporate strategies has become a prominent feature of the modern business landscape. As part of this movement, there is an increasing trend toward the monetary valuation of sustainability impacts. While the intention behind assigning monetary values to ESG-related impacts may be to provide a quantifiable basis for decision-making, it raises profound ethical concerns. This chapter explores the ethical dilemmas surrounding the monetary valuation of sustainability impacts, especially within the broader context of ESG performance measurement, in three problem dimensions: (1) the commodification of life and nature, (2) unequal power dynamics and neocolonial features of ESG and valuation, and (3) the marginalization of unquantifiable impacts and intrinsic values. The chapter ends by exploring the moral hazards that come with ignoring these ethical problems, and how corporate responsibility and accountability mechanisms can take these into account.

The advancement of technology has significantly altered the characteristics of remote work in general, and cross-border remote work in particular presenting complex regulatory challenges. These challenges, with their linkage to a large set of work arrangements and locations, involve among other matters coordinating the relationship between labor law and social security legislation. An increasing number of remote workers are now able to provide services across borders, to markets and countries where they or their employers have no physical connection. As there are more employment regulations to choose from, this increases the possibility for the employer to exploit lower labor standards in other countries and avoid responsibilities towards their workers. Analysis of the literature and jurisprudence in different cases shows that new interpretations of the place of work are brought forward with the view to better protect cross-border remote workers, both in Private International Law and in Labor Law, considering the increasingly virtual nature of the workplace. However, the principle of territoriality remains a strong argument in the hands of higher courts to limit evolution in that direction.

Unlike previous approaches to sustainable investing, focused primarily on excluding companies from problematic sectors such as tobacco, the aim of environmental, social, and governance (ESG) integration is to incorporate the assessment of ESG characteristics within mainstream investment analysis. This aim has given rise to claims that ESG integration is not about value judgments but focuses only on neutral risk–return calculations. Against such framing, this chapter argue that various ethical concerns inevitably arise when considering the quantification process underlying the generation of data used in ESG integration approaches. Drawing on the literature related to quantification and commensuration, the chapter identifies four areas in which ethical concerns can arise: (1) the strong focus on financial materiality; (2) the aggregation of disparate and often incommensurable ESG data; (3) ESG measurement problems; and (4) the treatment of ESG data as a private good. The chapter shows how quantification processes in these four areas give cause for ethical concerns related to which aspects of sustainability are rendered visible or invisible; how power relations between different field actors are structured by quantification; and which organizations have access to the opportunities that prevailing processes of quantification afford.

This chapter surveys the international legal framework governing transnational corporations (TNCs) and human rights. It begins with a brief history of the corporation, traces the rise of transnational corporate power since the 1970s, and offers a definition of the TNC. It then outlines the various ways in which corporate activities can adversely affect human rights, drawing on some of the most notorious incidents of recent decades. The chapter highlights the persistent difficulty of regulating corporations at the international level and describes the current regime under which states bear primary responsibility for preventing and remedying human rights abuses within their territories, including those committed by businesses. Since 2010, several states have introduced modern slavery legislation requiring companies to conduct due diligence on their operations and supply chains.

This chapter demonstrates R’s capabilities for statistical analysis and data science applications. It covers data importing from CSV/TSV files into R dataframes and computing basic statistics (mean, median, mode, variance, standard deviation) using built-in functions.

The chapter explores data visualization with ggplot2, creating histograms, bar charts, pie charts, and scatterplots for effective data presentation. Key statistical concepts include correlation analysis to measure variable relationships and statistical inference through hypothesis testing.

Practical statistical tests covered include t-tests for comparing two group means and ANOVA for comparing multiple groups. The chapter emphasizes R’s strengths in statistical computing, providing hands-on examples with real datasets and demonstrating how to interpret results for data-driven decision making.

Despite its explosive growth, there is considerable disagreement about the fundamental purpose of ESG. Two types of policies associated with ESG metrics and mechanisms give rise to at least two opposing views of their purpose: “profit-maximizing policies” versus “normative sustainable policies.” This chapter advocates the second type of strategy, arguing that corporate leaders who embrace ESG should be open to adopting a purpose that may undermine or even intentionally sacrifice shareholder wealth. In defending this view, the chapter considers the question of who has the legal, political, and moral authority to decide on ESG purposes. The chapter argues that business leaders already retain a great deal of legal autonomy in deciding whether or not to adopt some version of an ESG purpose as part of the firm's overall purposes. The chapter then discusses the challenges posed by what the authors call the Political Liberal Problem, which seems to suggest that corporate leaders should refrain from promoting a particular view of the good on behalf of their constituents or stakeholders. The chapter contends that a normative sustainable view of ESG purpose depends crucially on the ability to defend the relatively autonomous moral judgment of business leaders in setting ESG strategy.

This chapter examines the strategic techniques employed by CCMCEs to exploit jurisdictional arbitrage. It argues that modern multinational corporations are designed not merely as operational entities but as legal and financial structures optimized to separate the physical exchange of goods and services from their legal registration. This dislocation enables the manipulation of regulatory, tax, and reporting obligations through complex subsidiary networks. A dual-tier management system underpins this process: while first-tier executives define strategic objectives, second-tier legal, financial, and accounting professionals – ‘transaction cost engineers’ – design structures that maximize regulatory efficiency and minimize exposure. The chapter introduces key tools of this structuring process, including intermediary subsidiaries, split ownership patterns, and ‘end-of-chain’ entities. It conceptualizes CCMCEs as fractal organizations whose complexity emerges from recursive application of three ownership patterns: direct control, indirect control, and splitters. Through these forms, corporate groups engineer transactions to exist ‘elsewhere’, ideally in no single jurisdiction, allowing for regulatory evasion. The chapter also traces the rise of corporate treasury centres as organizational hubs for arbitrage.

This introductory chapter defines data science as a field focused on collecting, storing, and processing data to derive meaningful insights for decision-making. It explores data science applications across diverse sectors including finance, healthcare, politics, public policy, urban planning, education, and libraries. The chapter examines how data science relates to statistics, computer science, engineering, business analytics, and information science, while introducing computational thinking as a fundamental skill. It discusses the explosive growth of data (the 3Vs: velocity, volume, variety) and essential skills for data scientists, including statistical knowledge, programming abilities, and data literacy. The chapter concludes by addressing critical ethical concerns around privacy, bias, and fairness in data science practice.

This chapter introduces R, a free, open-source programming environment designed for data analysis and statistical computing. It covers R installation through RStudio IDE and demonstrates fundamental programming concepts including basic syntax, mathematical operations, and logical operators.

Key topics include data types (numeric, integer, character, logical, factor), data structures (vectors, matrices, lists), control structures (if-else statements, for/while loops), and functions for code organization and reusability.

The chapter emphasizes R’s advantages for data science: powerful statistical capabilities, extensive package ecosystem, and built-in data handling features. It concludes with R Markdown, which enables creation of professional reports combining code, output, and documentation in a single document for reproducible research and presentation.

This chapter advances the argument that jurisdictional arbitrage is not merely a tactic of tax or liability avoidance, but a distinct form of power – rooted in autonomy, control, and legal engineering – that enables CCMCEs to reshape their operating environments. Drawing on insights from corporate strategy, international political economy, and the CORPLINK project, it reframes arbitrage as a modality of power akin to the creation of a ‘hidden empire’. Through the modular structure of the CCMCEs, MNCs exploit legal and jurisdictional fragmentation to escape regulatory constraints, minimize costs, and amplify market valuation. This structural agility insulates them from state oversight, enabling them to strategically socialize costs and privatize gains. Unlike traditional theories that depict corporate power as relational or behavioural, the chapter argues for a third dimension: power as autonomy – the ability to opt out of constraints by rearranging legal structures. This form of rule-based transgression is monopolized by a global elite of corporations, investors, and advisory firms who exploit regulatory differences not by violating rules but by mastering them. Jurisdictional arbitrage, then, is a primary mechanism of modern corporate power and a central driver of global inequality.

This chapter explores supervised learning techniques where algorithms learn from labeled training data to make predictions. It begins with logistic regression for binary classification problems, using the sigmoid function to output probabilities between 0 and 1. Softmax regression extends this to multi-class problems. The chapter covers k-nearest neighbors (kNN), which classifies data points based on their similarity to training examples. Decision trees use entropy and information gain to create interpretable classification rules, while random forests combine multiple decision trees to reduce overfitting through ensemble methods. Naive Bayes applies Bayes’ theorem with independence assumptions for probabilistic classification, particularly effective for text classification. Finally, support vector machines (SVM) find optimal decision boundaries by maximizing margins between classes. Each technique is demonstrated through hands-on Python examples using real datasets, showing practical applications in various domains from healthcare to finance.

This introduction situates the volume within contemporary debates surrounding Environmental, Social, and Governance (ESG). It traces the historical evolution of the ESG movement—originally conceived as a voluntary form of regulation—from its origins in the early 2000s, associated with the launch of the UN’s Who Cares Wins initiative, to current developments marked by political backlash in the United States and regulatory consolidation in Europe. The authors argue that the widespread tendency to reduce ESG to issues of financial materiality—a view they describe as “mainstream ESG”—risks undermining its ethical and social foundations. Against this backdrop, the book advances the claim that ESG cannot be meaningfully developed without serious ethical reflection. The second part of the introduction presents the chapters included in this collection along three main lines: debates about the purpose(s) of ESG; discussions concerning the tensions between profitability and sustainability; and analyses of ESG as a form of voluntary or mandatory disclosure.