Energy-economy Models Research Articles

Scenario analysis of low-carbon development of energy industry with restriction of water resource in Xinjiang

Abstract This study empirically analyzes the prospects of the energy industry with the limited water resource in Xinjiang by extending an energy economic model. First, the past trends of water use in Xinjiang are analyzed. Energy utilization and industrial added value by sector are also investigated. Then, several important parameters such as economic growth rate, water saving rate, and energy saving rate are set exogenously. Especially, coal exploitation and utilization are selected as a typical case for studying energy development plan and technology choice. By keeping within the water requirement ‘red line’ and water security strategy, the acceptable speed and the future scale of the energy industry in Xinjiang are selected among several scenarios. Moreover, the interactions of economic growth, energy development, energy consumption, water requirement, and carbon dioxide emissions in Xinjiang are also analyzed. Finally, the technology choice of coal exploitation and utilization in Xinjiang with restriction of limited water resource is also suggested.

The role of carbon capture and storage electricity in attaining 1.5 and 2 °C

The climate targets defined under the Paris agreement of limiting global temperature increase below 1.5 or 2 °C require massive deployment of low-carbon options in the energy mix, which is currently dominated by fossil fuels. Scenarios suggest that Carbon Capture and Storage (CCS) might play a central role in this transformation, but CCS deployment is stagnating and doubts remain about its techno-economic feasibility. In this article, we carry out a throughout assessment of the role of CCS electricity for a variety of temperature targets, from 1.5 to above 4 °C, with particular attention to the lower end of this range. We collect the latest data on CCS economic and technological future prospects to accurately represent several types of CCS plants in the WITCH energy-economy model, We capture uncertainties by means of extensive sensitivity analysis in parameters regarding plants technical aspects, as well as costs and technological progress. Our research suggests that stringent temperature scenarios constrain fossil fuel CCS based deployment, which is maximum for medium policy targets. On the other hand, Biomass CCS, along with renewables, increases with the temperature stringency. Moreover, the relative importance of cost and performance parameters change with the climate target. Cost uncertainty matters in less stringent policy cases, whereas performance matters for lower temperature targets.

Impact of model resolution on scenario outcomes for electricity sector system expansion

Power sector capacity expansion models (CEMs) explore least-cost infrastructure trends under alternate techno-economic and policy scenarios. To maintain computational tractability when considering investment decisions over multiple decades, CEMs typically rely on a compact representation of annual hourly grid operations. Even as CEMs are being used to explore the role of variable renewable energy (RE) sources in the transition to a low-carbon grid, the required temporal resolution and operational detail in a CEM to adequately capture the fundamental economics of RE sources remains an open question. Here, we investigate the impact of embedding additional operational and temporal details in a CEM framework on the resulting projections for generation capacity additions and their utilization. Our approach is based on systematically comparing the outputs from a “chronological” CEM (C-CEM), which models annual grid operations using up to 12 representative days (288 h), with outputs from a commonly used “time slice” CEM (TS-CEM), using seasonally-averaged time blocks. The CEMs mainly differ in their representation of operational flexibility of thermal generators as well as the temporal resolution of load and RE generation. Studying the Texas grid over a range of hypothetical RE penetration scenarios, we find that, more often than not, the TS-CEM estimates higher solar capacity and lesser wind and natural gas capacity relative to the C-CEM, with 35% higher solar capacity projected by the TS-CEM in one scenario. We also test capacity projections of both CEMs through an hourly grid operations model to explore operational metrics, such as the ability to meet demand subject to intra and inter-annual variations in load and RE generation. This experiment reveals that C-CEM projections consistently lead to lower unmet demand compared to the TS-CEM capacity projections. These findings imply the need for sufficient temporal resolution and chronology or validated parameterizations that yield similar behavior to be included in power sector CEMs and multi-sector energy-economic models using a time slice representation.

A review of EROEI-dynamics energy-transition models

The need for an environmentally sustainable economy is indisputable but our understanding of the energy-economy interactions (dynamics) that will occur during the transition is insufficient. This raises fascinating questions on the future of economic growth, energy technology mix and energy availability. The crucial interactions between energy and economy systems can be usefully described in terms of the Energy Returned on Energy Invested (EROEI) metric (the energy cost of primary energy production). Multiple authors have used this metric to explore the behaviour of the economy over the transition to lower carbon energy sources. The following text is a review of energy-economy models that incorporate the EROEI metric. In particular, the EROEI-dynamics literature is found to describe a common set of dynamics associated with the transition to lower EROEI primary energy resources. These include: the rising resource-cost of primary energy production, the short-term misallocation of resources, the short-term overproduction of energy and the potential decline in economic stability. The literature can be divided into groups of related models. Following the review, a number of key areas for additional work are identified and discussed.

Capturing natural resource heterogeneity in top-down energy-economic equilibrium models

Top-down energy-economic modeling approaches often use simplified techniques to represent heterogeneous resource inputs to production. We show that for some policies, such as feed-in tariffs for renewable electricity, detailed representation of renewable resource grades is required to describe the technology more precisely and identify cost-effective policy designs. We demonstrate the hybrid approach for modeling heterogeneity in the quality of natural resource inputs required for renewable energy production in a stylized computable general equilibrium framework. Importantly, compared to the traditional approach, the hybrid approach resolves near-flat or near-vertical sections of the supply curve and improves the precision of policy simulation. We then represent the shape of a resource supply curve based on more detailed data. We show that for the case of onshore wind development in China, a differentiated feed-in tariff design that can only be modeled with the hybrid approach requires less than half of the subsidy budget needed for a uniform feed-in tariff design to achieve the same installation targets.

Substitution elasticities in an energy-augmented CES production function: An empirical analysis for Turkey

Abstract. This study estimates a production function for Turkey taking capital, labor and energy as input factors. The production function estimated is of the CES form with Hicks-neutral technology and constant returns to scale. A nonlinear least squares regression is employed on a dataset for the entire Turkish economy covering a time period of 27 years. The production function parameters provide insights into the elasticity of substitution of capital, labor and energy in Turkey. In particular, it is found that the elasticity of substitution between the capital-labor bundle and energy is, slightly higher than values found in other studies for various countries. This finding shows the relative ease of substitutability of capital-labor with energy for one another in Turkey and provides new insight on a critical parameter for future energy-economy modeling studies related to Turkey and other similar countries with no elasticity estimate. It is thought that the high substitutability for the case of Turkey may be related to the flexibility of its rapidly growing economy with investment needs that can easily be adapted to market conditions. Keywords. Substitution elasticities, CES, Energy economics. JEL. D22, E23, Q40.

Estimating the learning rate of a technology with multiple variants: The case of carbon storage

Learning rates enable the generation of first-order estimates of the cost of a technology as cumulative production grows, and play an important role in energy-economic modelling. This paper extends the component-based approach to estimating future learning rates to the case of a technology with multiple variants. It sets out a bottom-up method for estimating the composite learning rate including the situation where the proportional contribution of the different variants changes as cumulative production increases. The method is demonstrated for carbon storage using representative cost and distribution data from recent studies of storage in the European region.Carbon storage comprises four technology variants defined by the nature of the storage reservoir – (onshore or offshore) depleted oil & gas reservoirs and (onshore or offshore) saline aquifers – and each variant has a different learning rate reflecting its different cost structure. Moreover, the proportional contribution of each variant to total storage is likely to change with the growth of global storage capacity. The composite learning rate for carbon storage is estimated for scenarios in which the relative contributions change: a negative learning rate is determined in one scenario.

Model-based analysis of Intended Nationally Determined Contributions and 2 °C pathways for major economies

The 2015 Paris Agreement includes Intended Nationally Determined Contributions (INDCs) that describe the contribution of different nations to the global target of limiting world average temperature increase to levels well below 2° Celsius (2 °C). The current study uses the PROMETHEUS global energy-economy model to assess CO2 emission trajectories and energy system transformation pathways for Reference, INDC and cost-optimal 2 °C mitigation scenarios in four major economies (USA, EU, China, India) and globally. The 2 °C target implies a drastic reduction of emissions as well as significant transformation of the global energy system away from fossil fuels and towards Renewable Energy Sources (RES), and energy efficiency improvements. Comparison of 2 °C mitigation pathways with INDCs shows that in the EU and USA, the projected 2030 INDC emission targets are relatively close to the emissions reduction requirement of the cost-optimal 2 °C trajectory; however, in China, India and globally there is a large gap between INDCs and the optimal 2 °C pathway. Ambitious climate policies lead to reduced international fossil fuel prices, with bigger decline projected for coal price. National mitigation costs highly depend on the level of ambition of climate policies, but are generally projected to be lower than 2% of GDP over 2010–2050.

Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles

Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. Here, we develop state-of-the-art representations of consumer preferences in multiple global energy-economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to 2050. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary to drive widespread adoption of clean technologies. Carbon pricing alone is insufficient to bring low-carbon vehicles to the mass market, though it may have a supporting role in ensuring a decarbonized energy supply.

Coupling national and global models to explore policy impacts of NDCs

Since the official submission of the National Determined Contributions (NDCs) under the Paris agreement framework, a set of studies attempted to assess the energy and economic impacts on NDCs by using large-scale energy-economy models, both at the national and global level. This paper presents a novel approach to quantify policy impacts of NDCs, by combining a set of technology-rich country-level models for major economies with an economy-wide global CGE model. The methodology enhances the credibility of global model-based scenarios, by complementing it with detailed representation of country-level policy priorities and structural heterogeneities, captured by national-level models. It also improves the consistency between national short-to-medium term policy plans with global long-term climate objectives. Results indicate that energy efficiency improvements and increased penetration of RES are the main contributors to emission reduction induced by NDCs. The role of emission reduction options differentiates by country, depending on national socio-economic objectives, energy resource endowment and climate policy ambition. The Paris pledges are found to be consistent with robust economic growth both in developed and in emerging economies. NDCs result in a more labour-intensive economy driven by RES expansion, while major clean energy manufacturers (EU, China) have the potential to improve their balance of trade.

Interpreting energy scenarios

Quantitative scenarios from energy–economic models inform decision-making about uncertain futures. Now, research shows the different ways these scenarios are subsequently used by users not involved in their initial development. In the absence of clear guidance from modellers, users may place too much or too little confidence in scenario assumptions and results.

Estimating criteria pollutant emissions using the California Regional Multisector Air Quality Emissions (CA-REMARQUE) model v1.0

Abstract. The California Regional Multisector Air Quality Emissions (CA-REMARQUE) model is developed to predict changes to criteria pollutant emissions inventories in California in response to sophisticated emissions control programs implemented to achieve deep greenhouse gas (GHG) emissions reductions. Two scenarios for the year 2050 act as the starting point for calculations: a business-as-usual (BAU) scenario and an 80 % GHG reduction (GHG-Step) scenario. Each of these scenarios was developed with an energy economic model to optimize costs across the entire California economy and so they include changes in activity, fuels, and technology across economic sectors. Separate algorithms are developed to estimate emissions of criteria pollutants (or their precursors) that are consistent with the future GHG scenarios for the following economic sectors: (i) on-road, (ii) rail and off-road, (iii) marine and aviation, (iv) residential and commercial, (v) electricity generation, and (vi) biorefineries. Properly accounting for new technologies involving electrification, biofuels, and hydrogen plays a central role in these calculations. Critically, criteria pollutant emissions do not decrease uniformly across all sectors of the economy. Emissions of certain criteria pollutants (or their precursors) increase in some sectors as part of the overall optimization within each of the scenarios. This produces nonuniform changes to criteria pollutant emissions in close proximity to heavily populated regions when viewed at 4 km spatial resolution with implications for exposure to air pollution for those populations. As a further complication, changing fuels and technology also modify the composition of reactive organic gas emissions and the size and composition of particulate matter emissions. This is most notably apparent through a comparison of emissions reductions for different size fractions of primary particulate matter. Primary PM2.5 emissions decrease by 4 % in the GHG-Step scenario vs. the BAU scenario while corresponding primary PM0.1 emissions decrease by 36 %. Ultrafine particles (PM0.1) are an emerging pollutant of concern expected to impact public health in future scenarios. The complexity of this situation illustrates the need for realistic treatment of criteria pollutant emissions inventories linked to GHG emissions policies designed for fully developed countries and states with strict existing environmental regulations.

Time to refine key climate policy models

Ambition regarding climate change at the national level is critical but is often calibrated with the projected costs — as estimated by a small suite of energy–economic models. Weaknesses in several key areas in these models will continue to distort policy design unless collectively addressed by a diversity of researchers.

Modelling the energy transition: A nexus of energy system and economic models

Climate change induced policies impose wide-ranging implications throughout the whole energy system and influence various sectors of the economy. To analyse different decarbonization pathways for the energy system, existing models have traditionally focused on specific energy sectors, adopted specific research perspectives, assessed only certain technologies, or studied isolated components and factors of the energy system. However, few efforts have been undertaken to successfully model a broader picture of the energy-economic system. In this conceptual paper, we propose linking top-down and bottom-up models to represent: distributed generation and demand, operations of electricity grids, infrastructure investments and generation dispatch, and macroeconomic interactions. We review existing work on modelling the different dimensions of the energy transition to understand why models tend to focus on certain features or parts of the energy system. We then discuss methodologies for linking different type of models. We describe our integrated modelling framework, and the challenges and opportunities on linking models based on their capabilities and limitations.

U.S. energy sector impacts of technology innovation, fuel price, and electric sector CO2 policy: Results from the EMF 32 model intercomparison study

We study the impact of fuel prices, technology innovation, and a CO2 emissions reduction policy on both the electric power and end-use sectors by comparing outputs from four U.S. energy-economic models through the year 2050. Achieving innovation goals decreases CO2 emissions in all models, regardless of natural gas price, due to increased energy efficiency and low-carbon generation becoming more cost competitive. For the models that include domestic natural gas markets, achieving innovation goals lowers wholesale electricity prices, but this effect diminishes as projected natural gas prices increase. Higher natural gas prices lead to higher wholesale electricity prices but fewer coal capacity retirements. A CO2 electric power sector emissions cap influences electric sector evolution under reference technology assumptions but has little to no incremental influence when added to innovation goals. Long-term, meeting innovation goals achieves a generation mix with similar CO2 emissions compared to the CO2 policy but with smaller increases to wholesale electricity prices. In the short-term, the relative effect on wholesale prices differs by model. Finally, higher natural gas prices, achieving innovation goals, and the combination of the two, increases the amount of renewable generation that is cost-effective to build and operate while slowing the growth of natural-gas fired generation, which is the predominant generation type in 2050 under reference conditions.

Divestment prevails over the green paradox when anticipating strong future climate policies

Fossil fuel market dynamics will have a significant impact on the effectiveness of climate policies 1 . Both fossil fuel owners and investors in fossil fuel infrastructure are sensitive to climate policies that threaten their natural resource endowments and production capacities2–4, which will consequently affect their near-term behaviour. Although weak in near-term policy commitments5,6, the Paris Agreement on climate 7 signalled strong ambitions in climate change stabilization. Many studies emphasize that the 2 °C target can still be achieved even if strong climate policies are delayed until 20308–10. However, sudden implementation will have severe consequences for fossil fuel markets and beyond and these studies ignore the anticipation effects of owners and investors. Here we use two energy–economy models to study the collective influence of the two central but opposing anticipation arguments, the green paradox 11 and the divestment effect 12 , which have, to date, been discussed only separately. For a wide range of future climate policies, we find that anticipation effects, on balance, reduce CO2 emissions during the implementation lag. This is because of strong divestment in coal power plants starting ten years ahead of policy implementation. The green paradox effect is identified, but is small under reasonable assumptions. Fossil fuel market response to future climate policies could result in divestment in anticipation, or accelerated extraction—the green paradox. This study projects reduced emissions due to anticipation effects prior to policy implementation.

Modelling Sustainability Transitions: An Assessment of Approaches and Challenges

Transition modelling is an emerging but growing niche within the broader field of sustainability transitions research. The objective of this paper is to explore the characteristics of this niche in relation to a range of existing modelling approaches and literatures with which it shares commonalities or from which it could draw. We distil a number of key aspects we think a transitions model should be able to address, from a broadly acknowledged, empirical list of transition characteristics. We review some of the main strands in modelling of socio-technological change with regards to their ability to address these characteristics. These are: Eco-innovation literatures (energy-economy models and Integrated Assessment Models), evolutionary economics, complex systems models, computational social science simulations using agent based models, system dynamics models and socio-ecological systems models. The modelling approaches reviewed can address many of the features that differentiate sustainability transitions from other socio-economic dynamics or innovations. The most problematic features are the representation of qualitatively different system states and of the normative aspects of change. The comparison provides transition researchers with a starting point for their choice of a modelling approach, whose characteristics should correspond to the characteristics of the research question they face. A promising line of research is to develop innovative models of co-evolution of behaviours and technologies towards sustainability, involving change in the structure of the societal and technical systems.

An Energy-Economy Model for Indonesia Considering Inter-Regional Energy Resources Transportation and Daily Demand Changes

An Energy-Economy Model for Indonesia Considering Inter-Regional Energy Resources Transportation and Daily Demand Changes

An overview of salient factors, relationships and values to support integrated energy-economic systems dynamic modeling

Integrated energy-economic modelling is needed to support the development of energy and climate policies. This study asserts that it is important to consider a system dynamics modelling approach that includes dynamics, endogenous treatment of uncertainty and risks, and both aggregate economic and disaggregate technical or engineering levels of analysis. The study examined the economic growth and the factors of production, elasticities, macro- and technical substitutability; energy cost shares, heat engine efficiencies and energy services efficiencies. Emphasis was laid on the support of the future development of integrated energy-economic models covering (a) the key factors or components; (b) the relationships among these components; (c) a quantification of parameters; and (d) the implications for the development of an integrated energy-economic system dynamics model. The study suggested the following: a non-linear relationship in production and consumption; large variations among price and income elasticity values across time frames, across countries and regions, and across energy goods; a far from perfect substitution among factors of production and among energy goods on a macro-level; technical/engineering limits to substitution on a micro-level; and engineering and behavioural limits on what can be achieved with increased efficiencies. The study argues that integrated energy-economic modelling intensifies the accounting for the factors, relationships, quantifications, and implications, and that this practice allows for such models to describe a complex, emergent energy-economic reality that informs better energy policy.

The Role of Carbon Capture and Storage Electricity in Attaining 1.5 and 22C

The climate targets defined under the Paris agreement of limiting global temperature increase below 1.5 or 2°C require massive deployment of low-carbon options in the energy mix, which is currently dominated by fossil fuels. Scenarios suggest that Carbon Capture and Storage (CCS) might play a central role in this transformation, but CCS deployment is stagnating and doubts remain about its techno-economic feasibility. In this article, we carry out a throughout assessment of the role of CCS electricity for a variety of temperature targets, from 1.5 to above 4°C, with particular attention to the lower end of this range. We collect the latest data on CCS economic and technological future prospects to accurately represent several types of CCS plants in the WITCH energy-economy model, We capture uncertainties by means of extensive sensitivity analysis in parameters regarding plants technical aspects, as well as costs and technological progress. Our research suggests that stringent temperature scenarios constrain fossil fuel CCS based deployment, which is maximum for medium policy targets. On the other hand, Biomass CCS, along with renewables, increases with the temperature stringency. Moreover, the relative importance of cost and performance parameters change with the climate target. Cost uncertainty matters in less stringent policy cases, whereas performance matters for lower temperature targets.