This chapter analyzes the rise of Chinese âgeocriminalityâ in Latin America and the Caribbean, arguing that Chinese transnational organized crime cannot be understood separately from deepening ChinaâLAC economic and migratory ties. Moving beyond a narrow law-enforcement lens, it introduces the concept of a âSilk Road of Crime,â in which Chinese criminal networks exploit the same trade routes, diaspora communities, financial systems, and regulatory gaps that underpin legitimate engagement. Drawing on investigations by Earth League International, ProPublica, and others, the chapter documents four converging illicit markets: fentanyl precursor trafficking to Mexican cartels, low-cost âFlying Moneyâ laundering schemes, wildlife trafficking (including jaguar parts and shark fins), and human smuggling along âthe routeâ from China through Latin America to the US border. It details networks linked to Fujian-based groups and triads, highlighting patterns of crime convergence and the use of cryptocurrencies, shell companies, and diaspora businesses. While direct CCP command-and-control links remain limited, the chapter argues that selective enforcement, corruption, and strategic ambiguity create permissive conditions. Ultimately, Chinese geocriminality reflects the dark side of complex interdependence, reinforcing dependency structures and complicating US, Chinese, and regional cooperation.
Abstract The same infrastructures that enable decentralised finance, NFT markets, and metaverse platforms also create new spaces for paraâcrime. This article extends grey criminology to Web3 by applying three mechanisms of infrastructural illegality â parasitism, normative greyness, and platform coâproduction â first developed for physical crossâborder grey economies (daigou). Drawing on technical and financial crime literature, we show how smart contracts, stablecoins, and DAO governance are parasitised for money laundering and fraud; how technoâlibertarian narratives of 'code is law' and decentralisation sustain normative greyness; and how algorithmic security and DAO coâproduction reshape rather than eliminate paraâcrime. The analysis reveals both structural parallels with physical grey economies and domainâspecific variations â most notably, the deeper internalisation of coâproduction in codeâbased systems. We argue that grey criminology must extend its infrastructural turn to virtual and metaversal spaces, and that enforcement paradoxes â where suppression threatens valued infrastructures â apply as much to blockchain protocols as to customs thresholds.
Cryptocurrency investments are rapidly developing worldwide, including in Indonesia. Behind its profit potential, digital assets also open opportunities for criminals to commit money laundering offenses. The anonymity, pseudonymity, and decentralization of blockchain technology underlying cryptocurrencies create challenges for law enforcement in tracking illegal activities that exploit these assets. This study aims to examine the role of existing regulations in preventing the use of digital assets as a means of money laundering and to identify the challenges faced by law enforcement in enforcing rules against suspected cryptocurrency transactions. The research will analyze the extent to which the existing regulations, both at the national and international levels, are effective in preventing the use of cryptocurrencies for money laundering crimes. The second subtitle will explore various technical and legal constraints faced by law enforcement, including the lack of international cooperation, limitations of monitoring technology, and the low level of technical expertise among law enforcement officials.
Juan Ignacio Ibañez, Aayush Ladda, Paolo Tasca, Logan Aldred
The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the environmental effectiveness of Bitcoin mining bans by estimating the resultant carbon emissions from displaced mining operations. Our findings indicate that, contrary to policy goals, Bitcoin mining bans in low-emission countries can result in a net increase in global carbon emissions, a form of aggravated carbon leakage. We further explore the policy implications of these results, suggesting that more nuanced approaches may be required to mitigate the environmental impact of cryptocurrency mining effectively. This research contributes to the broader discourse on sustainable cryptocurrency regulation and provides a data-driven foundation for evaluating the true environmental costs of Bitcoin regulatory policies.
Catherine Carpentier-Desjardins, Masarah Paquet-Clouston, Stefan Kitzler, Bernhard Haslhofer
*PLEASE REFER TO THE SECOND VERSION UPLOADED IN JANUARY 2025. THIS VERSION CONTAINS A FEW DUPLICATES. VERSION 2 IS AVAILABLE FOR DOWNLOAD HERE: https://zenodo.org/records/14706760 README - Crime Events Dataset This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture". The following fields are included, each representing different aspects of the events collected. Data Fields 1. unique_key Description: A unique number assigned to identify each event in the dataset. 2. Agregators Description: The sources where the event is listed. Aggregators include: - De.Fi REKT - SlowMist - CryptoSec (rebranded to ChainSec as of February 2023) 3. DeFi actor involved Description: The name of the DeFi actor involved in the event (target, perpetrator, or intermediary). Sources: - On De.Fi REKT: Found as the "Title" of the eventâs listing. - On SlowMist: Found under the âHacked targetâ title. - On CryptoSec: Found in the "Title" of the eventâs listing with the date. 4. REKT URL Description: The URL to the event's listing on De.Fi REKT. Process: Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database 5. SlowMist URL Description: The URL to the event's listing on SlowMist. Process: Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL. 6. CryptoSec URL Description: The URL to the event's listing on CryptoSec. Process: Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events. 7. Aggregator Summary Description: A summary of the event provided by the aggregator. Sources: - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit". - On SlowMist: Under "Description of the event". - On CryptoSec: Below the title in quotation marks. 8. Aggregator sources URL Description: The URLs of references linked by the aggregator in the eventâs listing. Sources: - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link". - On SlowMist: Found by clicking "View Reference Sources". - On CryptoSec: Available by clicking the sourceâs name at the end of the summary. 9. Event date Description: The date the event occurred. Sources: - On De.Fi REKT: Listed under the "Date" field. - On SlowMist: At the top right of the listing. - On CryptoSec: Listed in parentheses behind the actorâs name. 10. Event year Description: The year the event occurred, extracted from the Event date. 11. Stolen amount USD Description: The total amount stolen, converted to USD. Sources: - On De.Fi REKT: Found under "Funds lost". - On SlowMist: Under the title âAmount of lossâ. - On CryptoSec: Behind the title "Amount stolen". Note: If needed, conversions were manually performed using CoinMarketCapâs historical data as explained in the paper. 12. Implication of actor Description: Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregatorâs summary and linked sources. 13. Strategy Description: The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources. 14. General tactic Description: The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources. 15. Specific tactic Description: The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources. 16. Paper category Description: The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor. 17. Stack category Description: The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT). --- For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation.
Purpose Cryptocurrencies have been used to commit various offences, but enforcement efforts remain underdeveloped relative to the value of these crimes. This paper aims to examine factors associated with outcomes of US-based cryptocurrency financial crime prosecutions. Design/methodology/approach The authors studied the 37 resolved cryptocurrency-based financial crime cases in the USA to date, exploring the impact of offence, defendant and evidence characteristics on the mode of disposition and penalties. The authors used bivariate analyses and logistic regression models to determine relationships among these variables. Findings The presence of individual defendants only (rather than a corporate defendant or combination thereof) and the use of only a cryptocurrency other than Bitcoin in committing a crime each made a case less likely to be resolved by dismissal, trial or summary or default judgement. Originality/value This paper is the first to examine variables contributing to financial crime prosecution outcomes and has implications for prosecutorial decision-making, resource allocation and the prevention and detection of financial offences involving cryptocurrencies.
The Covid-19 bear market presents the first acute market losses since active trading of Bitcoin began. This market downturn provides a timely test of the frequently expounded safe haven properties of Bitcoin. In this paper, we show that Bitcoin does not act as a safe haven, instead decreasing in price in lockstep with the S&P 500 as the crisis develops. When held alongside the S&P 500, even a small allocation to Bitcoin substantially increases portfolio downside risk. Our empirical findings cast doubt on the ability of Bitcoin to provide shelter from turbulence in traditional markets.
Prolific illegal timber trade between Russia and China cannot be stemmed by present certification schemes and Russian governmental strategies. Blockchain could present a new technological opportunity, simultaneously addressing illegal logging, regulating timber trade and meeting the needs of environmentally-sensitive markets. SWOT analysis of blockchain applicability in Russian-Chinese timber trade is presented. Decentralisation based on smart contracts could ensure timber supply chain transparency through each log cryptographic encryption. However, it is considered complicated as cryptocurrency (on which trade depends) is beyond the legislative sphere of both countries and energy demands are enormous. Beyond cryptocurrency prohibition, huge businesses and governments cannot accept blockchain for international trade because of transparency and cost risks. Digitalisation through blockchain implementation could provide a highly transparent, non-editable system to handle decentralised big data characteristic of the forestry economy, realise Industry 4.0 and develop the "China-Mongolia-Russia" economic corridor. Fundamentally, a workable Russian-Chinese Government-Science-Business model must be cultivated for effective blockchain implementation.
This thesis will examine the ways in which various cryptocurrencies have impacted certain traditional crimes. While crime is always evolving with technology, cryptocurrencies are a game changer in that they provide anonymous and decentralized payment systems which, while they can be tracked in a reactive sense via the blockchain, are seen by criminals as having better uses for them than traditional fiat currencies, such as the ability to send money relatively fast to another party without going through an intermediary, or the ability to obscure the origin of the money for money laundering purposes. Every week there are new cryptocurrencies flooding the market, and it doesnât look like it will abate any time soon. Blockchain technology, the underlying technology behind all cryptocurrencies, has uses that far surpass just the currency aspect. Criminals also see the potential that sending money anonymously, without a middleman beholden to regulations and tracking those transactions has. Any new technology while being revolutionary, will always trickle down to seedier elements of society who will always find a use for it. This paper will look at how cryptocurrencies have impacted drug trafficking, money laundering, and ransomware. I will also explore a new kind of crime called cryptojacking that has become possible because of cryptocurrency mining. Law enforcement may be playing a reactive and not a proactive role in the age of cryptocurrencies, but this paper will provide information that can be useful for law enforcement and applicable to their investigations.