Elsevier

Energy Policy

Volume 108, September 2017, Pages 12-20
Energy Policy

Financial stability at risk due to investing rapidly in renewable energy

https://doi.org/10.1016/j.enpol.2017.05.042Get rights and content

Highlights

  • Investing rapidly in renewable energy may put financial stability at risk.

  • The effect is especially pronounced in coal-dependent economies.

  • We study the optimal energy mix which does not compromise the financial system.

Abstract

We present novel insights about effective energy policies using an agent-based model. The model describes relevant feedback mechanisms between technological evolution, the interbank market and the electricity sector. Analysis with it shows that energy policies affect interbank connectivity and hence the likelihood of cascades of bank failures. This effect has not been studied before in the literature. In particular, we find that investments in renewable energy reduce interbank connectivity, increasing the probability of bank failures, while raising taxes on energy has an opposite effect. Increasing the share of renewable energy in electricity production initially increases the price of electricity, and thus improves profits and the ability to re-pay debts of incumbent power plants. However, when the share of renewable energy increases too quickly, financial stability may be at stake as the burden of financing investments in renewable energy offsets the improved profitability of existing power stations. All in all, this study provides a unique and novel perspective on the relationship between renewable energy investments and financial stability.

Introduction

Policymakers concerned with sustainability transitions need models capturing feedback mechanisms between different sub-systems of the economy, so that they can simultaneously assess economic, social and environmental performance of anticipated public policies and strategies. But current studies tend to examine climate change, financial instability or inequality without considering their complicated interrelationships. As a result, they are incapable of identifying indirect effects of sustainability policies in social, financial and economic realms. Hence they may overestimate the effectiveness of various policies, particularly by overlooking potential effects of policies directed at one sub-systems on other sub-systems. The proposed new approach avoids this deficiency by accounting for interactions between financial, energy and social subsystems. In particular, in this paper, we employ an agent-based model, capturing interactions between these interrelated systems.

The paper contributes to the literature on transitions to a low carbon economy, by assessing macro-economic impacts of associated policies. Most other studies adopt a more limited perspective, and as a result provide partial insights (Safarzynska et al., 2012). In order to understand the full implications of a transition, it is essential to adopt a macroeconomic perspective as we propose in the paper. A low-carbon transition requires changes that will have non-trivial impacts not only on energy systems but also on financial systems and even income distribution. Recent evidence shows that both inequality and energy prices affect financial stability (Russo et al., 2013, Cardaci and Saraceno, 2015, ESRB, 2016). This illustrates that the three sub-systems are intricately connected. Yet, so far, they have been studied separately. With our model we aimed to fill in this gap, and try to assess important secondary effects of a range of transition policies.

It is increasingly argued that to guide a transition to a low-carbon world, new models are needed that integrate knowledge of social processes with that of technical aspects of climate and energy systems (Nature Energy, 2016, Stern, 2016). Integrated assessment models are widely used tools in studies of macroeconomic impacts of climate policies. These models rely on very simplified assumptions of consumers’ and producers’ behavior, and ignore bounded rationality and social interactions. Agent-based modeling (ABM) offers realistic representations of socio-economic processes, which allows simulating the economy through interactions between large numbers of distinct agents. Over the last two decades, macro ABMs have been successfully applied to study financial contagion (Cincotti et al., 2010, Gaffeo et al., 2008, Delli Gatti et al., 2009, Neveu, 2013), technological evolution (Windrum and Birchenhall, 1998, Windrum and Birchenhall, 2005), and the relation between inequality, structural change and financial fragility (Russo et al., 2013, Cardaci and Saraceno, 2015). So far, very few macro ABMs include energy as an input of production (exceptions are Gerst et al. (2013) and Wolf et al. (2013)), while no model combines energy and financial systems.

In the light of this, we modify an agent-based model developed in Safarzynska and van den Bergh (2017) to study macroeconomic impacts of policies aimed at guiding the economy along sustainable trajectories. The model conceptualizes relevant feedback mechanisms between technological evolution, labor and interbank markets, and the electricity sector. In the model, four populations, namely of heterogeneous consumers, producers, power plants and banks, interact through interconnected networks. The modeling of sustainability policies requires several changes in the earlier model. In particular, we modify the model to explicitly account for: subsidies and investments in different energy mixes in electricity production; energy efficiency measures; energy taxation, whose revenues are used to reduce the tax burden on labor; and redistributive policies. We show that sustainability policies affect the relationship between the interbank connectivity and the probability of bank failures, which has not been considered so far in the literature. For instance, policies increasing the share of renewable energy in electricity production reduce the interbank connectivity, increasing the probability of bank failures; while raising energy taxes acts in the opposite way. So far, no study has examined this effect either empirically or theoretically.

A main insight of our study is that a too quick transition to renewable energy can pose a serious burden on the financial system. Investments in renewable energy increase the price of electricity, and hence profits and ability to re-pay debts of incumbent power plants. However, if the share of renewable increases too quickly, financial stability may be at stake as the burden of financing investments in expensive renewable power plants offsets the improved profitability of gas power stations. This is because the costs of constructing a renewable power plant per MW installed capacity is still considerably higher than that of a fossil-fuel power station. The detrimental effect of investments on the financial sector is especially pronounced in coal-dependent economies because investments required to set-up a coal power plants are larger than of CCGT. We will see that a solution is to combine renewable energy with combined cycle gas turbines (CCGT) in electricity production. This can improve the stability of the financial system.

The reminder of this paper is organized as follows. In Section 2, we describe the basic setup of the model and present a set of policy scenarios. Section 3 reports simulation results and interpretations. Section 4 concludes.

Section snippets

Model description

In this section, we describe the basic assumptions of, and modifications in, the model of Safarzynska and van den Bergh (2017). Fig. 1 presents a schematic structure of the model that highlights its modules and the described interactions between energy, labor and financial sub-systems. We consider a product market with many firms producing highly differentiated goods. A technological trajectory arises from the interplay between incremental innovation and the search for new product designs by

Results

We use the model to simulate the macroeconomic effects of different energy and social policies during 1000 time steps. The reported results are averages over 100 identical simulations. Mean results and standard deviations for each simulation setting are reported in the Appendix A. In Appendix D, we report parameter values used in the baseline scenario. Parameters in the baseline were chosen so that the model replicates a wide spectrum of stylized facts. In particular, our model is capable of

Conclusions and policy implications

This paper has presented novel insights about effective energy policies using an agent-based model. The model captures the coevolutionary nature of interactions between energy, technology and financial sectors, which allows us to study a broader than usual set of macroeconomic impacts of energy policies. Our results contribute to the debate on optimal timing and magnitude of investments in renewable energy. The power sector is currently responsible for nearly 40% of global carbon emissions (

Acknowledgments

The research was supported by the National Science Centre, Poland, grant 2013/08/S/HS4/00254.

References (41)

  • S. Wolf et al.

    A multi-agent model of several economic regions

    Environ. Model. Softw.

    (2013)
  • P. Windrum et al.

    Is life cycle theory a special case?: dominant designs and emergence of market niches through co-evolutionary learning

    Struct. Chang. Econ. Dyn.

    (1998)
  • U. Witt

    The dynamics of consumer behavior and the transition to sustainable consumption patterns

    Environ. Innov. Soc. Transit.

    (2011)
  • F. Allen et al.

    Financial contagion

    J. Political Econ.

    (2000)
  • K. Arrow et al.

    Are we consuming too much?

    J. Econ. Perspect.

    (2004)
  • S. Battiston et al.

    A climate stress-test of the financial system

    Nat. Clim. Change

    (2017)
  • T. Beck et al.

    Finance, inequality, and the poor

    J. Econ. Growth

    (2007)
  • R. Beddoe et al.

    Overcoming systemic roadbloacks to sustainability: the evolutionary redesign of worldviews, institutions and technologies

    PNAS

    (2009)
  • Cardaci, A. Saraceno, F., 2015. Inequality, financialisation and economic crises: an agent-based macro model. Working...
  • S. Cincotti et al.

    Credit money and macroeconomic instability in the agent-based model and simulator eurace

    Econ.: Open-Access Open-Assess. E-J.

    (2010)
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