Explaining and Predicting Double Tax Treaty Formation with Machine Learning Algorithms

The paper confronts country pairs that have tax treaties with country pairs that do not and investigates determinants for this distinction based on their gravity characteristics using machine learning techniques. It trains machine learning algorithms to distinguish such country pairs and selects random forest as the algorithm with the highest accuracy (94.3%) to use it for predictive purposes. The paper identifies 59 country pairs likely to have tax treaties based on their gravity characteristics. Countries/regions with the highest number of predicted new tax treaties are Germany (9), Saudi Arabia (8), Brazil (7), Myanmar (7), and Hong Kong (6). The paper investigates the machine learning prediction from the point of the current tax treaty status of the identified country pairs. Out of these country pairs, 31 are known to lead tax treaty negotiations, to have initialed a tax treaty, or to have already signed a tax treaty, 6 country pairs have signed or are negotiating an exchange of information agreement or a transport tax treaty, 3 country pairs used to have tax treaties, which were terminated. There is no public information available about ongoing negotiations for only 19 countries, less than a third of all countries where the machine learning algorithm would predict a treaty. This supports the validity of the machine learning techniques for prediction purposes. These results present important insights for policymakers when deciding over which treaty to pursue and which treaties may present a threat to a country’s international tax policy

Infrastructure for the Circular Economy: The Role of Policy in System Change

The concept of a Circular Economy has gained policy traction as a mainstream solution for preserving and renewing. This work sought to understand which policy instruments have been successful, might still be necessary, and are most likely in the future to deliver a high penetration Circular Economy for materials, wastes and associated energy. Waste related policy decisions in England over the past 20 years were reviewed, exploring the regulatory means that had achieved the current 44% diversion of municipal wastes from landfill. The hypothesis is that to achieve a far-reaching Circular Economy, at a greater scale, across different sectors and for a wider product range, suitable physical infrastructure (waste treatment assets) must be available to circulate those materials in the economy. Furthermore, the study set out to value financially and in terms of social and economic benefits that could be derived from a circular approach across the whole economy. We found the financial value to be significant at circa 1.5% of English GDP or  32bn and 72,000-175,000 jobs. It was demonstrated by calculation that DEFRA analysis (for England) underestimated the capacity gap for infrastructure by 11.3 million tonnes per annum in 2014. The policy findings were that the early implementation of blunt instruments such as a landfill tax and separate collection of recyclables did allow for diversion performance of approximately 45-50% recycling and composting, the remaining 50% of which was progressively treated by energy from waste rather than landfill. It is apparent that policies that drive system changes are effective in bringing about ecological transformations of how societies, economies, and governments operate at all levels. This ecological transformation is undoubtedly difficult to achieve since it will require consumers, manufacturers, regulators, policymakers, media, and business all to move differently and together, but nevertheless it is critical at a global scale.Open Acces

Taxation as a social engineering tool

This study provides policy makers with a legitimately supported basis upon which they can implement taxation to address socially harmful human activities in society. It does so through its novel three ‘Pillared’ approach, Ethics, Law and Economics, with a focus on addressing the causes of Human Induced Climate Change and Obesity

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Inward and Outward FDI Country Profiles, Second Edition

This second edition contains a series of 77 standardized country profiles dealing with the inward and outward foreign direct investment (FDI) performance of 40 economies. The profiles have been peer-reviewed by a global network of experts. The publication is intended to contribute to the analysis of trends in foreign direct investment and policy issues related to them. More specifically, the individual profiles discuss FDI trends and developments (country-level developments, the corporate players); effects of the recent global crises; and the policy scene. Each profile contains a standard set of tables, including on FDI stocks and flows, sectoral and geographical FDI distributions, the largest M&As and greenfield investments, the principal foreign affiliates (for inward FDI), and the principal multinational enterprises (for outward FDI). The standardized template used to produce the profiles allows cross-country comparisons. The volume is meant to be a reference tool for anyone interested in foreign direct investment