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Despite the importance of evaluating all mitigation options so as to inform policy decisions addressing climate change, a systematic analysis of household-scale interventions to reduce carbon emissions is missing. Here, we address this gap through a state-of-the-art machine-learning assisted meta-analysis to comparatively assess the effectiveness of a range of monetary and behavioral interventions in energy demand of residential buildings. We identify 122 studies and extract 360 effect sizes representing trials on 1.2 million households in 25 countries. We find that all the studied interventions reduce energy consumption of households. Our meta-regression evidences that monetary incentives are on an average more effective than behavioral interventions, but deploying the right combinations of interventions together can increase overall effectiveness. We estimate global cumulative emissions reduction of 8.64 Gt CO2 by 2040, though deploying the most effective packages and interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation with energy demand reductions and realizing substantial co-benefits.
Despite the importance of evaluating all mitigation options to inform policy decisions addressing climate change, a comprehensive analysis of household-scale interventions and their emissions reduction potential is missing. Here, we address this gap for interventions aimed at changing individual households’ use of existing equipment, such as monetary incentives or feedback. We have performed a machine learning-assisted systematic review and meta-analysis to comparatively assess the effectiveness of these interventions in reducing energy demand in residential buildings. We extracted 360 individual effect sizes from 122 studies representing trials in 25 countries. Our meta-regression confirms that both monetary and non-monetary interventions reduce the energy consumption of households, but monetary incentives, of the sizes reported in the literature, tend to show on average a more pronounced effect. Deploying the right combinations of interventions increases the overall effectiveness. We have estimated a global carbon emissions reduction potential of 0.35 GtCO2 yr−1, although deploying the most effective packages of interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation pathways with energy-demand reductions.
Electricity is a peculiar economic good, the most important reason being that it needs to be supplied at the very moment of consumption. As a result, wholesale electricity prices fluctuate widely at hourly or sub-hourly time scales, regularly reaching multiples of their average, and even turn negative. This paper examines whether the demand for electricity responds to such price variations in the very short term. To solve the classical identification problem when estimating a demand curve, we use weather-driven wind energy generation as an instrument. Our robustness checks confirm that wind energy is indeed a strong and valid instrument. Using data from Germany, we estimate that a 1 €/MWh increase in the wholesale electricity price causes the aggregate electricity demand to decline by 67–80 MW or 0.12–0.14%, contradicting the conventional wisdom that electricity demand is highly price-inelastic. These estimates are statistically significant and robust across model specifications, estimators, and sensitivity analyses. At average price and demand, our estimates correspond to a price elasticity of demand of about –0.05. Comparing situations with high and low wind energy (5–95th percentile), we estimate that prices vary by 26 €/MWh, and the corresponding demand response to wholesale electricity prices is about 2 GW, or 2.6% of peak load. Our analysis suggests that the demand response in Germany can be attributed primarily to industrial consumers.
While demand response is recognized as a useful tool for integrating renewable electricity, the related literature in developing countries has been limited. Meanwhile, the literature on demand side management (DSM) has ignored the value of agricultural demand as a demand side resource for integration of renewable energy. This article fills the gap by collecting agricultural load data from two distribution utilities in the Indian state of Gujarat and using it in a mixed-integer linear programming model to estimate the flexibility provided by agricultural DSM to the power system. Using a flexible load representation, the model chooses the optimal periods for agricultural supply subject to the constraints of meeting the irrigation needs of farmers and the marginal cost of electricity. This analysis shows that management of agricultural demand already reduces system costs by 4% or USD 6.09 per MWh of agricultural consumption. Going forward, with high shares of solar generation, shifting agricultural demand to daytime hours increases system flexibility. It reduces renewables curtailment by 4–7%, limits cycling costs of coal power plants, and reduces system integration costs by 22%. Agricultural DSM could be a cost-effective flexibility option in developing countries where only the least-cost options are economically viable.
This thesis discusses the role of the economic incentives and policies required to build energy systems that are compatible with achieving the goal of net-zero carbon emissions. I try to look beyond the techno-centric solutions that focus on new electricity generation technologies to explore how the way we consume and supply energy may be changed to facilitate the energy transition. The first paper of this cumulative thesis identifies packages of demand-side policy interventions in household energy consumption that can deliver an immediate reduction in carbon emissions. The second paper quantifies the relationship between electricity demand and prices in electricity markets using an instrumental variable approach. This relationship can provide the basis for designing policies that use electricity demand as a tool for integrating high shares of renewable energy in the power system. Following the theme of renewable energy integration, the third paper looks at the policies for influencing electricity demand from agricultural consumers in developing countries to increase the share of solar and wind while lowering the associated integration costs. And the last paper focuses on the need for policy instruments that determine the spatial allocation of renewable energy. The four papers together highlight that using the right economic incentives and policies, we can foster flexibility in both energy demand and supply that reduces the reliance on technological innovation and revolution for achieving the transformation to a low carbon energy system.
Electricity needs to be consumed at the very moment of production, leading wholesale prices to fluctuate widely at (sub-)hourly time scales. This article investigates the response of aggregate electricity demand to such price variations. Using wind energy as an instrument, we estimate a significant and robust short-term price elasticity of about −0.05 in Germany and attribute this to industrial consumers. As the share of consumption that is exposed to real-time prices (currently less than 25%) expands, we expect the aggregated price elasticity to grow.
Locational Investment Signals: How to Steer the Siting of New Generation Capacity in Power Systems?
(2020)
New generators located far from consumption centers require transmission infrastructure and increase network losses. The primary objective of this paper is to study signals that affect the location of generation investment. Such signals result from the electricity market itself and from additional regulatory instruments. We cluster them into five groups: locational electricity markets, deep grid connection charges, grid usage charges, capacity mechanisms, and renewable energy support schemes. We review the use of instruments in twelve major power systems and discuss relevant properties, including a quantitative estimate of their strength. We find that most systems use multiple instruments in parallel, and none of the identified instruments prevails. The signals vary between locations by up to 20 EUR per MWh. Such a difference is significant when compared to the levelized costs of combined cycle plants of 64–72 EUR per MWh in Europe.
Systematic review and meta-analysis of ex-post evaluations on the effectiveness of carbon pricing
(2024)
Today, more than 70 carbon pricing schemes have been implemented around the globe, but their contributions to emissions reductions remains a subject of heated debate in science and policy. Here we assess the effectiveness of carbon pricing in reducing emissions using a rigorous, machine-learning assisted systematic review and meta-analysis. Based on 483 effect sizes extracted from 80 causal ex-post evaluations across 21 carbon pricing schemes, we find that introducing a carbon price has yielded immediate and substantial emission reductions for at least 17 of these policies, despite the low level of prices in most instances. Statistically significant emissions reductions range between –5% to –21% across the schemes (–4% to –15% after correcting for publication bias). Our study highlights critical evidence gaps with regard to dozens of unevaluated carbon pricing schemes and the price elasticity of emissions reductions. More rigorous synthesis of carbon pricing and other climate policies is required across a range of outcomes to advance our understanding of “what works” and accelerate learning on climate solutions in science and policy.