Energy-economy Models Research Articles

Co-benefits of carbon and pollution control policies on air quality and health till 2030 in China

Facing the dual challenges of climate change and air pollution, China has made great efforts to explore the co-control strategies for the both. We assessed the benefits of carbon and pollution control policies on air quality and human health, with an integrated framework combining an energy-economic model, an air quality model and a concentration–response model. With a base year 2015, seven combined scenarios were developed for 2030 based on three energy scenarios and three end-of-pipe control ones. Policy-specific benefits were then evaluated, indicated by the reduced emissions, surface concentrations of major pollutants, and premature deaths between scenarios. Compared to the 2030 baseline scenario, the nationwide PM2.5- and O3-related mortality was expected to decline 23% or 289 (95% confidence interval: 220–360) thousand in the most stringent scenario, and three quarters of the avoided deaths were attributed to the end-of-pipe control measures. Provinces in heavily polluted and densely populated regions would benefit more from carbon and pollution control strategies. The population fractions with PM2.5 exposure under the national air quality standard (35 μg/m3) and WHO guideline (10 μg/m3) would be doubled from 2015 to 2030 (the most stringent scenario), while still very few people would live in areas with the WHO guideline achieved for O3 (100 μg/m3). Increased health impact of O3 suggested a great significance of joint control of PM2.5 and O3 in future policy-making.

Industrial decarbonization under Japan\u2019s national mitigation scenarios: a multi-model analysis

Energy-intensive industries are difficult to decarbonize. They present a major challenge to the emerging countries that are currently in the midst of rapid industrialization and urbanization. This is also applicable to Japan, a developed economy, which retains a large presence in heavy industries compared to other developed economies. In this paper, the results obtained from four energy-economic and integrated assessment models were utilized to explore climate mitigation scenarios of Japan’s industries by 2050. The results reveal that: (i) Japan’s share of emissions from industries may increase by 2050, highlighting the difficulties in achieving industrial decarbonization under the prevailing industrial policies; (ii) the emission reduction in steelmaking will play a key role, which can be achieved by the implementation of carbon capture and expansion of hydrogen technologies after 2040; (iii) even under mitigation scenarios, electrification and the use of biomass use in Japan’s industries will continue to be limited in 2050, suggesting a low possibility of large-scale fuel switching or end-use decarbonization. After stocktaking of the current industry-sector modeling in integrated assessment models, we found that such limited uptake of cleaner fuels in the results may be related to the limited interests of both participating models and industry stakeholders in Japan, specifically the interests on the technologies that are still at the early stage of development but with high reduction potential. It is crucial to upgrade research and development activities to enable future industry-sector mitigation as well as to improve modeling capabilities of energy end-use technologies in integrated assessment models.

Integration of an energy– economy model with an urban energy model

A proliferation of energy models has been developed across disciplines to explore energy and greenhouse gas (GHG) emissions-reduction strategies in cities. Hybrid models are especially useful because they incorporate more dynamics to simulate realistic results informed by relevant high-level policy decisions and building-level factors. Spatial and aspatial energy models, however, are not often linked, which overlooks the spatial impact of energy and emissions policies in urban environments. A new method is presented that links these types of models to understand how building stocks change over time in response to policies. This approach integrates outputs from an aspatial economic model, CIMS, with buildings in a spatially explicit urban building energy model (UBEM), UMI. The energy–economy model is parameterised against the UBEM using identified baseline condition and proposed future policy interventions. Building stock replacement and retrofit change are downscaled and disaggregated to individual buildings based on existing stock age and a probability-based Markov chain model (MCM). This integration enables simulations of cross-scale policy interventions that are sensitive to both economically and mechanically driven factors. An application of this approach shows how it can be used to evaluate how different policies interact with and influence building energy demand and GHG emissions. Practice relevance The results are integrated as a series spatially explicit energy modeling procedure (UMI) at the neighborhood scale. This process enables local assessments of efficacy of the proposed city scale and even regional policies in municipalities with various energy and GHG emission agendas. In the presented case study (of the Sunset neighborhood of Vancouver, BC, Canada) this method can quantify the elasticity of emission reductions from various urban form changes (e.g. infill, transportation-oriented development, etc.), new building code (i.e. BC Energy Step Code), active transportation and retrofit strategies from 2020 to 2050.

Policies for low-carbon and affordable home heating: A French outlook

Energy demand for residential heating is targeted in France by a number of subsidy programmes (tax credits, zero-interest loans, reduced VAT, white certificates) and the carbon tax. We assess the cost-effectiveness and distributional impacts of these policies using Res-IRF, an energy-economy model that integrates relevant economic, behavioural and technological processes. We find that, without further specification of revenue recycling, the carbon tax is the most effective, yet most regressive, policy. Subsidy programmes save energy at a cost of €0.05–0.08 per lifetime discounted kilowatt-hour, or €300–800/tCO2-eq; one euro of public money spent on subsidy programmes induces €1.0–1.4 private investment in home energy retrofits. Targeting subsidies towards low-income households, who tend to live in energy inefficient dwellings, increases leverage, thus reconciling economic efficiency and equity. The public cost of subsidies – €3 billion in 2013 – is outweighed by carbon tax proceeds from 2025 onwards, were the tax rate to grow as initially planned by the government. Meeting the long-term energy saving targets set by the government however requires adjusting subsidy programmes to better address rental housing. Lastly, an order-of-magnitude discrepancy between simulated and observed numbers of zero-interest loans points to economic and psychological barriers that require further investigation.

Modeling of thermal process in the energy system “Electrical network - asynchronous motor”

The paper discusses the influence of low-quality electricity on the temperature modes of operation of an asynchronous motor. In the course of experimental and analytical studies, the heat transfer coefficients and heat capacity of a particular electromechanical converter were determined. Experimental and analytical dependences of temperature changes of an asynchronous motor on time are given when it is connected to a supply voltage with different coefficients of sinusoidal distortion and negative sequence. The resulting model is tested for its adequacy to the real process and can be used as an element in the energy-economic model of an asynchronous motor to assess its uptime. This model that can be useful for simulation of thermal processes in asynchronous motors and optimising these devices for increasing the reliability.

The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition

Market players in the energy sector transition are heterogeneous, have bounded rationality and are influenced by their own past failures, as well as imitating the successes of their competitors. However this agent heterogeneity and complex behaviour in investment choices is not taken into account in traditional energy-economy models used to inform energy sector policies. By using BRAIN-Energy, an agent-based model of investment in electricity generation, which enables to study the impact of actors’ heterogeneous characteristics on the transition pathways of the UK, German and Italian electricity sectors, this paper shows how historic path-dependency in investment choices displaces low-carbon in favour of high-carbon investments under a weak regulatory framework. By contrast, imitation can help the diffusion of renewable technologies, through a self-reinforcing positive feedback when government subsidies to low-carbon investments are in place.

Evaluating long-term model-based scenarios of the energy system

Energy-economic models are used to provide science-based decision support in many contexts; evaluating scenarios generated by these models is critical for establishing the credibility of the analyses these tools support. We propose a framework for evaluating energy system scenarios consisting of three parts – a qualitative storyline, quantitative metrics, and evaluation criteria. We apply this framework to the reference scenario for GCAM-USA, a version of the Global Change Analysis Model with state-level detail in the United States, focusing on the evolution of the electric power sector. We develop new visual analytic tools to facilitate evaluation of model outcomes in 51 sub-national regions, and demonstrate how scenario performance can be tracked and compared across four quantifications of the GCAM-USA reference scenario.

The role of BECCS in deep decarbonization of China's economy: A computable general equilibrium analysis

Abstract Climate change has significant impacts on natural ecosystems and human living environments. Bioenergy with carbon capture and storage (BECCS) is a critical technology that can offer net CO2 emission reductions while providing a sustainable energy supply at lower costs. The role of the BECCS deployment in achieving deep decarbonization in China needs to be quantified. Scenario analyses were conducted in this study based on a computable general equilibrium energy-economic model, the China-in-Global Energy Model (C-GEM), with detailed representations of BECCS. The results showed that BECCS will capture 0.59 gigatonnes of CO2 in 2050 under the 2 °C Scenario and 0.95 gigatonnes of CO2 in 2050 under the 1.5 °C Scenario, respectively. If these emission targets have to be reached without BECCS, the carbon price and gross domestic product (GDP) losses will be higher. Especially under the 1.5 °C Scenario, the deployment of BECCS can reduce the carbon price by 61% and the GDP loss rate by three percentage points in 2050 compared with that without the deployment of BECCS.

Energy Policies for Eco-Friendly Households in Luxembourg: a Study Based on the LuxHEI Model

In the Grand Duchy of Luxembourg, the residential building sector is a major energy consumer and greenhouse gases emitter that plays a key role in achieving the country’s environmental objectives. The purpose of this work is to assess the effectiveness of the most important policy instruments in decreasing the final energy consumption and direct CO2 emissions of Luxembourgish households. To this end, we developed the LuxHEI model, which is an enhanced and upgraded version of the well-known French simulation model Res-IRF. This variant has also been adjusted to the particular problems of a small country with growing economy and a quickly increasing population. The LuxHEI model goes beyond standard energy-economy models by incorporating global warming as a decision-making factor. The model outcomes reveal that in 2060, and compared with the no-policy baseline scenario, the most aspirational policy mix enables energy savings of 42% and emission reductions of 60%. However, in none of the projections, the residential building sector meets the national energy and climate targets on time. From the results we can draw the following policy implications: for a significant improvement of the sector’s energy efficiency and sufficiency, the implementation of a remediation duty for existing buildings and the tightening of the performance standards for new constructions, together with the application of a national carbon tax, are crucial.

Comparing transformation pathways across major economies

This paper explores the consequences of different policy assumptions and the derivation of globally consistent, national low-carbon development pathways for the seven largest greenhouse gas (GHG)–emitting countries (EU28 as a bloc) in the world, covering approximately 70% of global CO2 emissions, in line with their contributions to limiting global average temperature increase to well below 2 °C as compared with pre-industrial levels. We introduce the methodology for developing these pathways by initially discussing the process by which global integrated assessment model (IAM) teams interacted and derived boundary conditions in the form of carbon budgets for the different countries. Carbon budgets so derived for the 2011–2050 period were then used in eleven different national energy-economy models and IAMs for producing low-carbon pathways for the seven countries in line with a well below 2 °C world up to 2050. We present a comparative assessment of the resulting pathways and of the challenges and opportunities associated with them. Our results indicate quite different mitigation pathways for the different countries, shown by the way emission reductions are split between different sectors of their economies and technological alternatives.

The low carbon development options for Russia

Russia is one of the largest carbon emitters in the world, possessing huge resources of both fossil fuels and zero-carbon energy sources. The Paris Agreement targets require substantial efforts to limit global warming to “well below 2 °C”. Energy-economic modeling provides sound conclusions that continuation of existing energy and climate policy will lead to stabilization of energy carbon emissions in Russia at the current level in 2010–2050 (about 30% below 1990). Stronger mitigation policies could gradually reduce domestic energy CO2 emissions by 61% from 2010 to 2050 (75% below 1990). Deep decarbonization policies with even more ambitious commitments could ensure an 83% reduction in energy CO2 emissions from 2010 levels (88% below 1990) by 2050. All key sectors (energy, industries, transport, and buildings) can play a substantial role in decarbonizing the national economy. However Russia’s historical reliance on domestic consumption and exports of fossil fuels creates strong barriers to decarbonization. Emission reduction costs are expected to be below 29 USD/tCO2 by 2030, 55 USD/tCO2 by 2040, and 82 USD/tCO2 by 2050 in the most ambitious decarbonization scenario. The results of this study provide insights into how Russia can enhance its ambitions to implement the Paris Agreement and contribute to global efforts toward building a climate-neutral economy by 2050.

A Scalable Energy–Economy Model for State-Level Policy Analysis Applied to a Demand-Side Management Program

Over the past two decades, states and cities implemented low-carbon energy development, renewable portfolio standards, demand-side management (DSM), renewable energy production incentives, green building requirements, regional carbon trading agreements, and other energy-based economic development initiatives. Yet the dearth of state-level and substate-level models makes it difficult to predict the effects of such actions. This article addresses this shortcoming by presenting the performance results of the new Indiana Scalable Economy and Energy Model (IN-SEEM)—a model utilizing a dynamic, simultaneous equations framework—and demonstrates the model’s capabilities with an analysis of electricity price increases from a DSM program in the state of Indiana. Overall performance of the model is strong, with high adjusted R2 values and low mean absolute percent errors for most of 30 endogenous variables. A DSM price increase analysis finds variation in impact across the state’s 10 major economic sectors and small changes in energy consumption.

Electric sector impacts of renewable policy coordination: A multi-model study of the North American energy system

Policies to encourage renewable electricity generation have grown at national and subnational levels. These measures are often characterized by geographical fragmentation, as jurisdictions typically select their own renewable targets without coordinating with neighboring regions. However, the literature on renewable policies has not examined the effects of cross-border interactions and coordination, especially in a multi-model comparison to examine robustness to structural and parametric uncertainties. This paper assesses the impacts of regional and international renewable policy coordination on economic, environmental, and planning outcomes in the North American power sector. Using a multi-model comparison with eight energy-economic models, the analysis demonstrates how prospective renewable mandate trade formulations impact power sector outcomes like capacity planning decisions, costs, emissions, trade, and infrastructure investments. Model results suggest that renewable policy coordination can lower costs by up to 20% for the stringencies examined here. Fragmentation lowers gas-fired generation, but coal and nuclear are also displaced, especially when regions comply without trade. Policy costs decrease for the U.S. with higher regional and international coordination, but magnitudes vary by model. Restricting coordination leads to higher capacity investments, and absent incentives to enhance efficiency, grids do not share resources to balance variability. Transmission investments and trade are highest with international policy coordination.

Rechargeable Batteries for Simultaneous Demand Peak Shaving and Price Arbitrage Business

The goal of this study is to assess the economics of deploying rechargeable batteries for simultaneous demand peak shaving and price arbitrage business. To this end, we present a novel modeling approach for investment and operational decision-making, which supports the selection of battery technology and size among different Li-ion batteries and optimizes the scheduling of battery charge and discharge. Battery degradation incurring due to repeated charging and discharging is accounted for by implementing the Rainflow cycle counting algorithm in an energy-economic modeling framework for operational dispatch optimization. This allows for an accurate representation of cycle battery degradation in the optimization model. Simulation results obtained for a Swiss electricity service provider show that battery deployment is currently only economically viable if the sizes of energy capacity and power units are selected judiciously. Furthermore, our case-specific findings reveal that the cycle lifetime of a battery has only a minor impact on the achievable life cycle revenues, which can be attributed to the fact that a small number of charge and discharge cycles is sufficient to exploit the most profitable revenue opportunities. Based on the conducted case study, we argue that second-use batteries could be particularly appealing for stationary energy storage.

Value of technology in the U.S. electric power sector: Impacts of full portfolios and technological change on the costs of meeting decarbonization goals

Abstract Power sector decarbonization is an important pillar of climate mitigation efforts, but perspectives differ about the relative competitiveness of generation technologies and how limited portfolio approaches (e.g., where non-renewable generation options are prohibited) could alter the costs and likelihoods of reaching emissions reduction goals. The existing literature on impacts of technological availability and cost on electric sector planning typically use models that do not have sufficient technological, spatial, and temporal detail to adequately resolve the economic competitiveness of variable renewable energy vis-a-vis dispatchable generators. Using a state-of-the-art energy-economic model, this work examines impacts of technological availability and advanced generation technologies on U.S. electricity market outcomes across a range of regional, market, and policy contexts. We show that decarbonization costs are 11%–76% higher as technological options are removed from consideration (incremental compliance costs for a 95% CO2 reduction below 2005 levels are roughly twice as high when new nuclear, carbon-capture-equipped units, and transmission are not allowed). However, the economic and technical implications of limited portfolios depend on the market and policy contexts (e.g., costs are higher with stringent targets, more extensive end-use electrification, and lower gas prices) and the costs and capabilities of the remaining options. The analysis demonstrates how lower temporal and spatial resolution models likely understate the value of technology by omitting key economic and technical features of high variable renewable pathways. Additionally, the analysis quantifies how technological change can lower costs of emissions reductions by 7%–73% and how low-cost battery storage can provide a hedge against higher costs when technological portfolios are limited.

Energy consumption and economic growth modelling in SADC countries: an application of the VAR Granger causality analysis

Energy consumption and economic growth modelling in SADC countries: an application of the VAR Granger causality analysis

Assessment for Economic Impact by Dynamic Multi-sector Energy Economic Model Considering Engineering Characteristics of Automobile Industry and Power Sector

Assessment for Economic Impact by Dynamic Multi-sector Energy Economic Model Considering Engineering Characteristics of Automobile Industry and Power Sector

Efficacité économique et effets distributifs de long-terme des politiques de rénovation énergétique des logements

Using the Res-IRF energy-economy model, we evaluate the impact of four key energy efficiency subsidy programmes and the carbon tax on energy demand for residential heating in France. The carbon tax is found to be the most effective, yet also most regressive, instrument. Subsidies have a leverage effect of 1 to 1.4 in 2015. Targeting subsidies towards low-income households, that tend to live in energy inefficient dwellings, increases leverage, thus reconciling economic efficiency and social equity. Meeting the long-term energy saving targets set by the French government will require maintaining subsidy programmes until 2050 and extending them to rented dwellings.

Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model

Empirical studies on technology advancement and regional diffusion indicate that technology learning is a multi-scale process driven by factors such as globally traded equipment and local accumulation of experience. This understanding should be incorporated in energy-economy models to improve the model representation and policy recommendations. The present study augments the large-scale, integrated assessment model REMIND for this purpose. To answer the research question on how multi-level learning affects technology diffusion and the regional costs of mitigation policies, alternative variants of multi-scale learning are implemented and calibrated to one set of regional-specific cost data. The results show that purely local learning leads to similar technology diffusion patterns as fully global learning, since the learning rates are set equal, and all regions are calibrated to their cost levels and specific capacity. Relative to these two, the combination of global and local learning leads to slower deployment of learning technologies and increases the mitigation cost if the cost disparity persists across regions, e.g. due to incomplete spillover. Our modelling exercise suggests that the choice of learning rates at different levels matter and calls for better data quality.

Economic drivers of wind and solar penetration in the US

Much has been made of the potential for wind and solar generation to supply cheap, low-emissions electricity, but considerable disagreement exists as to which combinations of many potential drivers will enable deep penetration of these technologies. Most existing analyses consider limited factors in isolation, such as investment costs or energy storage, and do not provide rigorous support for understanding which combinations of factors could underpin a leading role for wind and solar. This study addresses this gap by undertaking a systematic sensitivity analysis using a state-of-the-art energy-economic model to comprehensively evaluate the relative magnitudes of five key drivers that may influence future wind and solar deployment in the United States. We find future wind and solar capital costs and carbon policy are the dominant factors, causing the average wind and solar share to vary by 38 and 31 percentage points, respectively. Transmission and storage availability have much smaller effects, causing the average share to vary by no more than 15 and 5 percentage points, respectively. No single factor unilaterally determines wind and solar deployment. The variable renewable share of electricity generation never reaches 100% nationally in any scenario even with low-cost storage, as decreasing marginal returns at higher deployments eventually outpace cost reductions. Average wind and solar shares and ranges of possible outcomes are higher in this study relative to recent multi-model comparison studies due to lower renewable costs and the potential for more stringent policies. Understanding drivers and barriers to renewable deployment has important ramifications for technology developers, infrastructure, market design, and policymakers, and this research provides insights as to which combinations of drivers lead to the greatest share of economic wind and solar deployment and why.