An Agent-Based Model of Endogenous Technological Change -- An Extension to the Grubler-Gritsevskyi Model

Based on earlier, pioneering work done at IIASA, this paper presents a model of endogenous technological change under the three potential most important "stylized facts": increasing returns to adoption, uncertainty, and heterogeneous agents following diverse technology development and adoption strategies. As an intermediary step towards the final, long-term research objective of developing a multi-agent model, this paper deals with two heterogeneous agents, a risk-taking one and a risk-aversion one. Interactions between the two agents include trade on resource and good, and technological spillover ("free-riding" and technology trade). With the two heterogeneous agents, we run optimization to minimize their aggregated costs to find out what rational behaviors are under different assumptions if the two agents are somehow cooperative. The global optimal solutions of the two-agent model are of Pareto optimality in the sense that none of the two could be made better off without the other being made worse off. The simulations show how agent heterogeneity - different risk attitudes and sizes, trade between agents and technological spillover effect influence the technological change process. Finally this paper plots and analyzes emission paths as results of different technological change process

Complexity and the Economics of Climate Change: a Survey and a Look Forward

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/cesdp2016.htmlDocuments de travail du Centre d'Economie de la Sorbonne 2016.58 - ISSN : 1955-611XWe provide a survey of the micro and macro economics of climate change from a complexity science perspective and we discuss the challenges ahead for this line of research. We identify four areas of the literature where complex system models have already produced valuable insights: (i) coalition formation and climate negotiations, (ii) macroeconomic impacts of climate-related events, (iii) energy markets and (iv) diffusion of climate-friendly technologies. On each of these issues, accounting for heterogeneity, interactions and disequilibrium dynamics provides a complementary and novel perspective to the one of standard equilibrium models. Furthermore, it highlights the potential economic benefits of mitigation and adaptation policies and the risk of under-estimating systemic climate change-related risks

Carrots and sticks for new technology: Abating greenhouse gas emissions in a heterogeneous and uncertain world

Many governments use technology incentives as an important component of their greenhouse gas abatement strategies. These “carrots” are intended to encourage the initial diffusion of new, greenhouse-gas-emissions-reducing technologies, in contrast to carbon taxes and emissions trading which provide a “stick” designed to reduce emissions by increasing the price of high-emitting technologies for all users. Technology incentives appear attractive, but their record in practice is mixed and economic theory suggests that in the absence of market failures, they are inefficient compared to taxes and trading. This study uses an agent-based model of technology diffusion and exploratory modeling, a new technique for decision-making under conditions of extreme uncertainty, to examine the conditions under which technology incentives should be a key building block of robust climate change policies. We find that a combined strategy of carbon taxes and technology incentives, as opposed to carbon taxes alone, is the best approach to greenhouse gas emissions reductions if the social benefits of early adoption sufficiently exceed the private benefits. Such social benefits can occur when economic actors have a wide variety of cost/performance preferences for new technologies and either new technologies have increasing returns to scale or potential adopters can reduce their uncertainty about the performance of new technologies by querying the experience of other adopters. We find that if decision-makers hold even modest expectations that such social benefits are significant or that the impacts of climate change will turn out to be serious then technology incentive programs may be a promising hedge against the threat of climate change.climate change, technology policy, uncertainty, agent-based modeling, exploratory modeling, social interactions

Does Endogenous Technical Change Make a Difference in Climate Policy Analysis? A Robustness Exercise with the FEEM-RICE Model

Technical change is generally considered the key to the solution of environmental problems, in particular global phenomena like climate change. Scientists differ in their views on the thaumaturgic virtues of technical change. There are those who are confident that pollution-free technologies will materialize at some time in the future and will prevent humans from suffering the catastrophic consequences of climate change. Others believe that there are inexpensive technologies already available and argue the case for no-regret adoption policies (e.g. subsidies). Others again believe that the process of technological change responds to economic stimuli. These economic incentives to technological innovation are provided not only by forces that are endogenous to the economic system, but also by suitably designed environmental and innovation policies. In this paper, we consider and translate into analytical counterparts these different views of technical change. We then study alternative formulations of technical change and, with the help of a computerized climate-economy model, carry out a number of optimization runs in order to assess what type of technical change plays a role (assuming it does) in the evaluation of the impact of climate change and of the policies designed to cope with it.Climate policy, Environmental modeling, Integrated assessment, Technical change

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

Complexity and the Economics of Climate Change: A Survey and a Look Forward

Climate change is one of the most daunting challenges human kind has ever faced. In the paper, we provide a survey of the micro and macro economics of climate change from a complexity science perspective and we discuss the challenges ahead for this line of research. We identify four areas of the literature where complex system models have already produced valuable insights: (i) coalition formation and climate negotiations, (ii) macroeconomic impacts of climate-related events, (iii) energy markets and (iv) diffusion of climatefriendly technologies. On each of these issues, accounting for heterogeneity, interactions and disequilibrium dynamics provides a complementary and novel perspective to the one of standard equilibrium models. Furthermore, it highlights the potential economic benefits of mitigation and adaptation policies and the risk of under-estimating systemic climate change-related risks

A review of agent-based modelling of climate-energy policy

Unidad de excelencia María de Maeztu CEX2019-000940-MAltres ajuts: Russian Science Foundation. Grant Number: 19-18-00262Agent-based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behaviour than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and non-market interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate-energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation and emissions trading through adoption subsidies to information provision tools such as smart meters and eco-labels. Our treatment pays specific attention to behavioural assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy question