Hierarchies and Universal Inclusion in Scientific Communities

This chapter explains the genesis of inequalities and hierarchies in modern science. It studies the forms and mechanisms of scientific communication on the basis of which the social structures of science are built: publications, authorship, co- and multiple authorship, citations as units of information and as social rewards, peer review as evaluation of publications (and of projects and careers). This is a network of institutions that seems to guarantee universal access to participation in science to all those who fulfill basic conditions. But the chapter demonstrates how in all these institutional dimensions differences arise between successful and not equally successful participations. Success generates influence and social attractiveness (e.g. as a co-author). Influential and attractive participants are recruited into positions where they assess the achievements of others and thereby limit and control inclusion in publications, funding and careers. Equality at the start is transformed into hierarchies of control. Finally, the chapter asks for potential alternative control structures that transform a conservative hierarchy into decentralized ‘market’ controls that involve everyone in a more dynamic production and evaluation of scientific achievements.

REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits

This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity.

Law and Economics, or Punctum Archimedis in Environmental Law

This article examines the autonomous field of Law and Economics, indicates the importance of integrating the economical methodology in the process of comprehensively understanding the normative order and laments over the undesirable shortage of interdisciplinary education and academic dialogue regarding Law and Economics in Georgia. Moreover, this paper deliberates on the relevance of the Coase theorem for lawyers and policy-makers, displays the specific recommendations of its application in jurisprudence, including elucidating property rights, maximizing economic productivity and minimizing transaction costs. The following article discusses attempts and shortcomings to harmonize a decentralized market solution in environmental law and demonstrates regarding the example of the EU emission trading system that the method of Law and Economics can be used not only for a thorough analysis of the existing model but also as a pragmatic instrument for defining a normative program.

Introduction

The central puzzle of the book is introduced: Why is there so much consensus among shareholders of large firms? Although trading on the market forges agreement between shareholders about the firms’ objectives, residual yet substantial disagreement should persist because of market failures. To resolve the puzzle, a vision of the political economy of the (publicly traded) firm is outlined, showing 1) how the upstream operation of a decentralized market mechanism makes collective decision-making in the firm easier and more stable; and 2) why the democratic (majority) principle is likely to promote economic efficiency as a politically stable choice. Yet more is needed: a vision of the social economics of shareholders showing how collective decision-making shapes individual preferences in a way that promotes the alignment of shareholders. These two visions are laid out in the two parts of the book.

Queueing Dynamics and State Space Collapse in Fragmented Limit Order Book Markets

In modern equity markets, participants have a choice of many exchanges at which to trade, each of which typically operates as an electronic limit order book. This paper analyzes this setting as a multiclass queueing system. Taking into account the effect of investors’ order-routing decisions across exchanges, they find that the equilibrium of this decentralized market exhibits a state space collapse property, whereby the queues at different exchanges are coupled, and the overall behavior of the market is captured through a one-dimensional process that can be viewed as a weighted aggregate queue length across all exchanges. The key driver of this coupling phenomenon is anticipated delay as opposed to the queue lengths themselves.

Containing Carbon Through Cap-and-Trade or a Per-Unit Tax

Carbon has been part of the Earth since its beginning, and the carbon cycle is well understood. However, its abundance in the atmosphere has become a problem. Those who propose solutions in decentralized market economies often prefer economic incentives to direct government regulation. Carbon cap-and-trade programs and carbon tax programs are the prime candidates to reign in emissions by altering the economic conditions under which producers and consumers make decisions. Under ideal conditions with full information, they can seamlessly remove the distortion caused by the negative externality and increase a society’s welfare. This distortion is caused by overproduction and underpricing of carbon-related goods and services. The ideal level of emissions would be set under cap-and-trade, or be the outcome of an ideally set carbon tax. The ideal price of carbon permits would result from demand generated by government decree meeting an ideal fixed supply set by the government. The economic benefit of using the ideal carbon tax or the ideal permit price occurs because heterogeneous decision-makers will conceptually reduce emissions to the level that equates their marginal (incremental) emissions-reduction cost to the tax or permit price. When applying the theory to the real world, ideal conditions with full information do not exist. The economically efficient levels of emissions, the carbon tax, and the permit price cannot be categorically determined. The targeted level of emissions is often proposed by non-economists. The spatial extent and time span of the emissions target need to be considered. The carbon tax is bound to be somewhat speculative, which does not bode well for private-sector decision-makers who have to adjust their behavior, and for the achievement of a particular emissions target. The permit price depends on how permits are initially distributed and how well the permit market is designed. The effectiveness of either program is tied to monitoring and enforcement. Social justice considerations in the operation of tax programs often include the condition that they be revenue-neutral. This is more complicated in the permit scheme as much activity after the initial phase is among the emitters themselves. Based on global measurement of greenhouse gases, several models have been created that attempt to explain how emissions transform into concentrations, how concentrations imply radiative forcing and global warming potential, how the latter cause ecological and economic impacts, and how mitigation and/or adaptation can influence these impacts. Scenarios of the uncertain future continue to be generated under myriad assumptions in the quest for the most reliable. Several institutions have worked to engender sustained cooperation among the parties of the “global commons.” The balance of theory and empirical observation is intended to generate normative and positive policy recommendations. Cap-and-trade and carbon tax programs have been designed and/or implemented by various countries and subnational jurisdictions with the hope of reducing carbon-related emissions. Many analysts have declared that the global human society will reach a “tipping point” in the 21st century, with irreversible trends that will alter life on Earth in significant ways.

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.