Over the past decade, corporate investors have increasingly recognized that responsible environmental and social practices are essential to long-term financial success. Despite growing interest, corporate practices remain largely unchanged, and planetary trends are deeply concerning. This review applies a systems analysis lens to understand how financial sector structures and actors influence sustainability outcomes, often in counterproductive ways. Key barriers include the lack of science-based metrics, poor integration of environmental risks, and limited capacity to evaluate complex system dynamics. Current financial practices frequently miss or misinterpret systemic sustainability risks. We identify three critical areas where collaboration between sustainability science and finance is urgently needed: (1) translating scientific insights into financial decision contexts, (2) supporting science-based corporate sustainability reporting, and (3) strengthening environmental impact assessment. Systems thinking helps clarify where financial leverage can drive meaningful, cross-scale change—an essential step toward aligning capital flows with long-term ecological resilience and sustainability goals.

Emotions relate to climate change action in various ways. Here we elaborate on how the expansion of digital social networks and advances in artificial intelligence, ranging from recommender systems to generative AI, may affect the way people perceive and engage emotionally on climate change. We develop a simple framework that links individual and collective emotions, AI, and climate action, and suggest three critical areas in need of further investigation.

The threat associated with climate change and nature degradation poses complex financial challenges. Our systematic literature review of 88 finance-related publications published between 2015 and early 2022 revealed a gap in research on nature-related financial risks and their connections to climate change, particularly regarding ocean-related risks beyond rising sea levels. Although methods are available to assess these risks, more standardized approaches are needed. Based on this literature review, we developed a typology of climate-nature-finance effects using nine nested causal loop diagrams (CLDs). Our typology illustrates how climate change and environmental degradation can create chain reactions or domino-effects impacting insurance coverage, investors’ confidence, and market stability, leading to broader economic instability. This typology can help practitioners and scholars analyze their exposure to climate change and ecological degradation. Additionally, it can contribute to developing alternative quantitative assessments for studying non-linearities in financial risks. Future research can benefit from addressing the interactions between climate change and nature degradation more effectively and exploring the effects of finance on the environment and society.

At a time when the world must cut greenhouse gas emissions precipitously, artificial intelligence (AI) brings large opportunities and large risks. To address its uncertain environmental impact, we propose the ‘Earth alignment’ principle to guide AI development and deployment towards planetary stability.

This book offers a critical primer on how Artificial Intelligence and digitalization are shaping our planet and the risks posed to society and environmental sustainability.

As the pressure of human activities accelerates on Earth, so too does the hope that digital and artificially intelligent technologies will be able to help us deal with dangerous climate and environmental change. Technology giants, international think-tanks and policy-makers are increasingly keen to advance agendas that contribute to “AI for Good” or “AI for the Planet." Dark Machines explores why it is naïve and dangerous to assume converging forces of a growing climate crisis and technological change will act synergistically to the benefit of people and the planet. It explores why AI and associated digital technologies may lead to accelerated discrimination, automated inequality, and augmented diffusion of misinformation, while simultaneously amplifying risks for people and the planet. We face a profound challenge. We can either allow AI accelerate the loss of resilience of people and our planet, or we can decide to act forcefully in ways that redirects its destructive direction.

This urgent book will be of interest to students and researchers with an interest in Artificial Intelligence, digitalization and automation, social and political dimensions of science and technology, and sustainability sciences.

The combination of large volumes of climate and environmental data and analytical methods using machine learning has a long history in research about environmental sustainability. The last years, however, signal a rapid shift in the use of artificial intelligence (AI) for environmental sustainability research and action. AI is however only one part of more profound technological changes. In addition, as the pressure of human activities accelerates on the living planet and the climate system, so too does the hope that a wide variety of technologies that support digitalization such as sensors, advanced satellite technologies, and increased uses of AI will be able to increase societies’ capacities to detect and respond to the repercussions of a rapidly changing planet.

Ecosystems around the world are showing symptoms of resilience loss. With them there is an increasing risk of critical transitions or regime shifts: large, abrupt and difficult to reverse changes in the function and structure of ecosystems. When regime shifts occur they often impact the flow of benefits that people get from nature, and with them the ability of companies, cities or nations to satisfy human needs. Here we ask who is exposed to ecological regime shift risks, and by being exposed, who has the agency or power to intervene and perhaps avoid tipping points?

To answer this question we match companies whose activities imply the use or extraction of natural resources in places vulnerable to regime shifts. First, we use Earth observations to quantify resilience loss in marine systems. We also used temeprature records to quantify the probability of extreme and severe heat wave events in the oceans. Both are conditions that can reduce primary productivity and impact fisheries. Then, we identify vessels that fish in these areas of the world and match their owners and shareholders when available in public databases.

For publicly listed companies we reconstruct social networks of companies ownership and investments. The networks serve to identify financial actors exposed to ecological tipping points through several investments or regions of the world. The multilayer network can be centred around companies, shareholders, investors, or countries. Clustering at different levels of aggregation allow us to identify actors with disproportional risk exposure, but also companies, investors or countries who could make a difference in mitigating risk.

Extreme weather-related events like wildfires have been increasing in frequency and severity due to climate change. Public online conversations that reflect on these events as climate emergencies can create awareness and build support for climate action but are also used to spread misinformation and climate change denial. To what extent automated social media accounts—“social bots”—amplify different perspectives of such events and influence climate change discourses, remains unknown, however. We use Twitter and the 2019/2020 Australia bushfires as a case study to explore this issue. Utilizing more than 1 million Tweets, we identify how climate change is framed in the context of those fires, and to what extent social bots affect specific climate change frames, including the spread of misinformation. Our results show that climate change represents a substantial part of online conversations about fires. The bushfires are primarily framed as a climate change issue including its measurable impacts and political perspectives. Climate denial represents a small share of this conversation and receives limited amplification. Social bots seemingly contribute to the climate change conversation, both through frames that support and oppose climate action, and amplify to larger degree frames appealing to emotions, such as sympathy or humor. We also find that Twitter discussions about the role of social bots in spreading climate denial are amplified more than actual climate-critical frames propagated by bots. A complex interplay between social bots, Twitter conversations, and online news media is emerging, which shapes discussions about climate change and wildfires.

Background: Emerging and re-emerging infectious diseases (EIDs), such as Ebola virus disease and highly pathogenic influenza, are serious threats to human health and wellbeing worldwide. The financial sector has an important, yet often ignored, influence as owners and investors in industries that are associated with anthropogenic land-use changes in ecosystems linked to increased EIDs risks. We aimed to analyse financial influence associated with EIDs risks that are affected by anthropogenic land-use changes. We also aimed to provide empirical assessments of such influence to help guide engagements by governments, private organisations, and non-governmental organisations with the financial sector to advance a planetary health agenda.

Methods: For this integrative analysis, we identified regions in the world where there was evidence of a connection between EIDs and anthropogenic land-use changes between Nov 9, 1999, and Oct 25, 2021, through a targeted literature review of academic literature and grey literature to identify evidence of drivers of anthropogenic land-use change and their association with commodity production in these regions. We only included publications in English that showed a connection between deforestation and the production of one or more commodities. Publications merely describing spatial or temporal land-use change dynamics (eg, a reduction of forest or an increase of palm-oil plantations) were excluded. As we were assessing financial influence on corporate activities through ownership specifically, we focused our analysis on publicly listed companies. Equity data and data about ownership structure were extracted from Orbis, a company information database. We assessed financial influence by identifying financial entities with the largest equity ownership, descriptively mapping transboundary connections between investors and publicly listed companies.Findings 227 public and private companies operating in five economic sectors (ie, production of palm oil, pulp and wood products, cocoa, soybeans, and beef) between Dec 15, 2020, and March 8, 2021, were identified. Of these 227, 99 (44%) were publicly listed companies, with 2310 unique shareholders. These publicly listed companies operated in six geographical regions, resulting in nine case-study regions. 54 (55%) companies with complete geographical information were included in the countries network. Four financial entities (ie, Dimensional, Vanguard, BlackRock, and Norway's sovereign wealth fund) each had ownership in 39 companies or more in three of the case-study regions (ie, north America, east Asia, and Europe). Four large US-based asset managers (ie, Vanguard, BlackRock, T Rowe Price, and State Street) were the largest owners of publicly listed companies in terms of total equity size, with ownership amounts for these four entities ranging from US$8 billion to $21 billion. The specific patterns of cross -national ownership depended on the region of interest; for example, financial influence on EIDs risks that was associated with commodity production in southeast and east Asia came from not only global asset managers but also Malaysian, Chinese, Japanese, and Korean financial entities. India, Brazil, the USA, Mexico, and Argentina were the countries towards which investments were most directed. Interpretation Although commodity supply chains and financial markets are highly globalised, a small number of investors and countries could be viewed as disproportionally influential in sectors that increase EIDs risks. Such financial influence could be used to develop and implement effective policies to reduce ecological degradation and mitigate EIDs risks and their effects on population health.