Quantifying the effects of uncertain climate and environmental policies on investments and carbon emissions: A case study of Chile
Introduction
In 2014 the Intergovernmental Panel on Climate Change concluded that changes in climate due to global warming are a major threat to humans, a large number of economic activities, and most ecosystems on earth (Field et al., 2014). In response to this message, many countries and regions have enacted or are discussing the implementation of a series of environmental and economic policies that aim at reducing greenhouse gas emissions across all economic and industrial activities. In the electricity industry, these policies include carbon taxes and cap-and-trade programs (Chen and Tseng, 2011), Renewable Portfolio Standards (RPSs) (Lyon and Yin, 2010), feed-in tariffs (Couture and Gagnon, 2010), production tax credits (Wiser et al., 2007), and energy efficiency programs (Arimura et al., 2011), among others. To date, more than 100 countries have either binding or voluntary renewable targets (REN21, 2015) and nearly 40 national jurisdictions have put a price on carbon emissions (Kossoy and Guigon, 2012).
A salient feature of these policies is that they are highly uncertain, which complicates investment decisions for both power transmission planners and generation firms. Part of the uncertainty of climate policies is a direct consequence of the uncertainty of multi-decadal climate forecasts and the potential effects of increasing levels of greenhouse emissions on the atmosphere (Allen et al., 2000, Murphy et al., 2004, Tol, 2003). Naturally, as prediction models improve and new information becomes available, policy makers can adjust the stringency of environmental regulations in order to mitigate the effects of climate change in the most economically efficient manner. Furthermore, the use of different methodologies can also have relatively large impacts on, for instance, estimates on the social cost of carbon emissions (Tol, 2008, Ackerman and Stanton, 2012), which are ultimately used to guide environmental policies such as carbon taxes.
A changing political landscape also leads to uncertain environmental policies. For instance, generation firms and transmission planners in the US now face large regulatory uncertainties given the goal of the current administration to basically dismantle most of the environmental and climate policies enacted during the two previous administrations (Kraft, 2017). In Europe, the UK has had a history of inconsistent energy policies to reduce greenhouse gas emissions and to incentivize investments and generation from renewable energy technologies (Newbery, 2016). Spain, a country that fostered a large amount of investments in renewables through feed-in tariffs, decided to drastically reduce the subsidies to solar PV installations by nearly 30% in 2011 after the financial crisis, which likely affected investment decisions in the Spanish electricity market (Hofman and Huisman, 2012).
As it has been demonstrated in prior studies, an optimal portfolio of investments for one scenario of market, regulatory, and technological conditions can be highly suboptimal if the future turns out to be different to the planned one (van der Weijde and Hobbs, 2012, Munoz et al., 2014, Munoz et al., 2015). In particular, more uncertainty increases the risk of stranded transmission and generation assets (Wright, 2012).
In this paper, we focus on quantifying how uncertain climate policies as a consequence of changing political landscapes can affect both transmission and generation investments in Chile. We define a set of five different policy scenarios inspired by current regulations in Chile and on international policies and use a two-stage stochastic transmission and generation planning model with recourse to find optimal investment policies. Under the assumption of a perfectly competitive generation market, aligned objectives, inelastic demand, and proactive transmission investments that are 100% paid for by demand, this solution is equivalent to an equilibrium on investments and operations for a deregulated generation market. We employ this model to estimate bounds on the cost of uncertain climate and environmental policies. The lower bound results from the assumption that all generation firms and the transmission planner will develop plans considering all scenarios explicitly, which minimizes the expected system cost of meeting future demand and climate policies. The upper bound results from imposing deterministic investment plans, assuming that both generators and the transmission planner will act myopically, ignoring uncertainty.
Our results indicate that, for our set of scenarios, the cost of uncertain climate and environmental policies for the Chilean system is between 3.2% ($1.8 Billion, lower bound) and 5.7% ($3.3 Billion, upper bound) of the optimal total system cost of the stochastic model. Furthermore, uncertain policies can result in nearly 18% higher CO2 emissions than under perfect information if agents explicitly account for uncertainty in their decisions using a stochastic planning model, which could result in a high regret if more stringent emissions policies are imposed in the future. Consequently, an uncertain political landscape can impose a rather high cost in the Chilean power system, even if all agents plan investments using sophisticated planning tools such as stochastic programming.
The main message for regulators and planning authorities is that, if possible, policy uncertainties should be reduced to a minimum. Otherwise, planning with explicit consideration of uncertain policies would yield the lowest expected system cost. For planning authorities that are also responsible for shaping long-term energy and environmental policies, such as in Chile, this means that transmission infrastructure should be selected taking into consideration that these policies could change significantly in the near future under a different political administration or as new information becomes available. To our best knowledge, this is the first study that quantifies the investment effects and costs of uncertain climate and environmental policies on investments, costs, and carbon emissions in Chile, taking investments in transmission and generation as a response to these policies. Yet, models that are similar to the one we use in this study have been employed before in other contexts (e.g., to quantify the value of stochastic instead of deterministic transmission planning models in regions of Europe (van der Weijde and Hobbs, 2012) and the US (Munoz et al., 2014)).
We structure the rest of the paper as follows. In Section 2 we present a literature review focused on previous research studies that have quantified the effect climate change and climate change policy uncertainty on generation and transmission investment decisions. In Section 3 we present a two-stage stochastic transmission and generation planning model. In Section 4 we summarize our case study. This includes a simplified representation of the Chilean power system and our assumptions of policy scenarios. In Section 5 we present our numerical results and analyze their implications. Finally, in Section 6 we conclude and provide some policy recommendations.
Section snippets
Literature review
Climate change will have a large impact in the energy sector (Parkpoom et al., 2005). The rise of temperatures, changes in precipitation, humidity variation, and the number of sunny days per year will affect both the consumption and production of energy (Feenstra et al., 1998). It is expected that consumption patterns of electricity will change as average temperatures increase (Franco and Sanstad, 2008, Hamlet et al., 2010). Multiples studies have shown that climate change will lead to a
Methodology
We quantify the effect of uncertain climate and environmental policies using two different equilibrium models. One is deterministic and assumes that all market agents have access to perfect information. The other one is stochastic and assumes that all agents are risk-neutral and make decisions trying to minimize expected system cost (transmission planner) or maximize expected profits (generation firms). We model uncertainty of climate and environmental policies using a set of scenarios that
Case study
Our case study is a network reduction of the Chilean power system. We considered the two largest power systems in the country that currently operate in a single synchronized grid known as the National Electric System (SEN) operated by the national system operator: The Northern Interconnected System (SING) and the Central Interconnected System (SIC). The model consists of 28 buses, 36 transmission elements, and 351 aggregated generators with an installed capacity of 22.74 GW. We model investment
Results
The model outlined in Section 3.2 is a mixed-integer linear program that was formulated in AIMMS version 4.25 (64-bit) and solved using CPLEX 12.6.3 in a computer with an Intel Core i5-2450 M CPU @ 2.50 GHz with 6GB of RAM. The size of the stochastic optimization model is 3.2 million variables (555 integer) and 3.4 million constraints. We solved all optimization problems to a 0.5% optimality gap,9
Conclusions
Several countries around the world have already enacted or are considering implementing climate change or environmental policies to reduce greenhouse emissions, particularly in the electric power industry. Unfortunately, changing political agendas and uncertain estimates of the social costs of CO2 emissions and other pollutants make investment planning in new transmission and generation infrastructure very difficult, mainly because these are long-lived assets.
In this paper we use a two-stage
Acknowledgments
The research in this article was supported by FONDECYT #11150029, CONICYT/FONDAP/15110019 (SERC-CHILE), and CONICYT-Basal Project FB0008. We thank the editor and two anonymous reviewers for their constructive comments, which helped us to improve earlier versions of this manuscript.
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