Energy-economy Models Research Articles

Reducing the cost of capital to finance the energy transition in developing countries

Climate stabilization requires the mobilization of substantial investments in low- and zero-carbon technologies, especially in emerging and developing economies. However, access to stable and affordable finance varies dramatically across countries. Models used to evaluate the energy transition do not differentiate regional financing costs and therefore cannot study risk-sharing mechanisms for renewable electricity generation. In this study, we incorporated the empirically estimated cost of capital differentiated by country and technology into an ensemble of five climate–energy–economy models. We quantified the additional financing cost of decarbonization borne by developing regions and explored policies of risk premium convergence across countries. We found that alleviating financial constraints benefits both climate and equity as a result of more renewable and affordable energy in the developing world. This highlights the importance of fair finance for energy availability, affordability and sustainability, as well as the need to include financial considerations in model-based assessments.

Mutual reinforcement of land-based carbon dioxide removal and international emissions trading in deep decarbonization scenarios

Carbon dioxide removal (CDR) technologies and international emissions trading are both widely represented in climate change mitigation scenarios, but the interplay among them has not been closely examined. By systematically varying key policy and technology assumptions in a global energy-economic model, we find that CDR and international emissions trading are mutually reinforcing in deep decarbonization scenarios. This occurs because CDR potential is not evenly distributed geographically, allowing trade to unlock this potential, and because trading in a net-zero emissions world requires negative emissions, allowing CDR to enable trade. Since carbon prices change in the opposite direction as the quantity of permits traded and CDR deployed, we find that the total amount spent on emissions trading and the revenue received by CDR producers do not vary strongly with constraints on emissions trading or CDR. However, spending is more efficient and GDP is higher when both CDR and trading are available.

Harnessing offshore wind for decarbonization: A geospatial study of hydrogen production and heavy industry utilization in Mexico

This study comprehensively examines the techno-economic potential of utilizing offshore wind resources for hydrogen production in Mexico. Through the integration of geospatial analysis techniques with energy generation and economic models, the research estimates hydrogen production, CO2eq emissions reduction potential, Levelized cost of electricity (LCOE), and Levelized cost of hydrogen (LCOH). The analysis highlights Mexico’s capacity to produce up to 217.33 million tons of hydrogen annually, predominantly from the Gulf of Mexico. Centralized offshore hydrogen production demonstrates significant cost advantages, being up to 0.92 USD/kg cheaper than onshore production. The study also underscores the economic benefits of repurposing existing oil and gas infrastructure, which can reduce LCOH by up to 11%. Environmental benefits are substantial, with hydrogen use in ammonia production potentially reducing CO2eq emissions by 82.5%. Additionally, the study identifies highly suitable offshore areas for hydrogen production, particularly near existing oil infrastructure, facilitating the integration of new renewable energy projects with minimal additional infrastructure development. These findings provide valuable insights for policymakers and stakeholders, emphasizing the economic viability and environmental benefits of offshore wind hydrogen production as a strategic pathway for Mexico’s energy transition and decarbonization efforts.

Evaluation of energy economic optimization models using multi-criteria decision-making approach

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

Assessment of PV excess generation savings in Ghana considering peer-to-peer energy trading

ABSTRACT Solar is one of the Renewable (RE) sources with a high electricity generation potential. However, the challenge with using individual stand-alone Photovoltaic (PV) systems is the mismatch between load demands and electricity generated such that surplus and insufficient energy are experienced by prosumers. This study used the Net Present Value (NPV), and levelized cost of energy (LCOE) economic models to assess the impact of modern (peer-to-peer, P2P) trading and applicable PV system cost reduction strategy in Ghana, considering a cluster of stand-alone systems. The energy demand and generations of a total of 7 prosumer and consumer buildings in the Don Bosco Technical Institute, Ashaiman, were used. The results, considering a mini-grid connection between the buildings, showed an LCOE of GH₵ 1.358 over 10 years and NPV of about GH₵ 5 million for 25 years when excess generation is traded as compared to GH₵ 2.423 for LCOE and about GH₵ 180,000 NPV when excess generation is disregarded. A proposed shared investment community-owned system design showed further LCOE reduction to GH₵ 1.122 because the capital cost decreased. This shows that P2P energy trading can make PV generation attractive amidst other benefits like job creation, revenue generation and emissions reduction if RE is used. Lastly, a suitable energy trading market mechanism was proposed for implementing P2P in the context of Ghana. Though the research revealed that more benefits can be obtained from PV systems in Ghana, future technical analysis regarding transmission losses, the Internet of Things (IoTs), and blockchain need to be done.

Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 μg·m-3 and 1.8-8.9 μg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Techno-economics and environmental sustainability of agricultural biomass-based energy potential

This paper explores the viability of utilizing agricultural biomass-based energy potential, employing mathematical, engineering, and economic modeling techniques. Moreover, the potential of a biogas-based co-digestion (CD) system, integrating its techno-economic performance and environmental sustainability in terms of electricity generation, has also been studied. In this investigation, the categorization of 25 different plant species into two groups: arable field crops (AFCs) and horticultural plants (HPs), was performed. Data was collected during the 2021‐–2022 cropping season in Bangladesh from various sources, including literature reviews, governmental, and non-governmental organizations. The findings revealed that the available agricultural biomass residues, totaling 1,02,585.75 KT, have the capacity to generate 1,33,815 million m3/year of biogas. This energy potential corresponds to 291,125.85 TJ/year or 9231.60 MW of electricity, which can fulfill 88% of the national total energy demand. In terms of levelized cost, the proposed approach is more competitive and shows a greater promise compared to other technologies. Furthermore, it demonstrates environmental friendliness by reducing CO2 emissions by 156 tons at a cost of $7/ton while earning $1092 annually from the potential carbon-credit market. This approach presents a potential solution to address Bangladesh's energy crisis. The payback period of the system ranged from 2.93 to 3.75 years, with and without the inclusion of a slurry, respectively. The recommended methods hold significant promise for meeting national energy demands. A case study was provided as a proof-of-concept (PoC) to validate the approach. This study is the first of its kind, providing valuable insights into the renewable energy potential in Bangladesh. The results will assist policymakers in formulating sustainable energy policies.

ЩОДО МОЖЛИВОСТІ ВИБОРУ ЗАСОБІВ ЗАХИСТУ АСИНХРОННОГО ДВИГУНА У ПРОМИСЛОВИХ УМОВАХ

The article is devoted to the urgent problem of choosing the means of protection of asynchronous motors (AM) operating in the shop electric networks of industrial enterprises with low-quality electricity. The well-known energy-economic model of an inducti

Global energy consumption of the mineral mining industry: Exploring the historical perspective and future pathways to 2060

The mining industry globally is responsible for significant energy consumption, and is an important source of greenhouse gas emissions. Considering that future mineral demand is likely to increase and that the final energy consumption per unit mass of mineral extracted (energy intensities of mining) is also forecast to increase as a result of a decrease in mineral resource deposit qualities, the mining industry’s final energy consumption will increase in the future. But the scale of that future increase remains unexplored. In this study, we (i) provide the first bottom-up assessment of the mining industry’s final energy consumption globally (1971–2015), (ii) use 1.5°C consistent socio-economic scenarios to conduct an exploratory study of future possible pathways for the mining industry’s final energy consumption, and (iii) review the extent to which such energy consumption is considered in energy-economy models.We find that the mining industry is currently responsible for approximately 1.7% of global final energy consumption. However, the mining industry’s final energy consumption is likely to increase significantly, by a factor in the range 2–8 by 2060, depending on the future economic trajectory, on the evolution of energy intensities, and on future recycling rates. We also find that mineral material flows and their associated energy requirements (including the mining industry’s energy consumption) are insufficiently covered in many energy-economy models. Our work suggests that the limited representation of material flows and associated energy requirements is currently an important blind spot in energy-economy modelling and may hinder the efforts of the community to build consistent energy transition pathways.

Equity implications of net-zero emissions: A multi-model analysis of energy expenditures across income classes under economy-wide deep decarbonization policies

With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energy-economy models using harmonized policy scenarios reaching economy-wide, net-zero CO2 emissions across the United States in 2050. We employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and net-zero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.

Economic Dispatch of Island Integrated Energy System Based on Ranking Group Learning Optimization Algorithm

In order to improve the flexibility of the Island Integrated Energy System (IIES) and reduce operating costs, the Cooling Thermal and Electric three-load Demand Response (DR) are introduced into the IIES. Under the constraints of the output of each unit, this paper establishes a comprehensive energy economic dispatch model with the lowest total operating cost of the system as the objective function, and a Ranking Group Learning Optimization (RGSO) is proposed to optimize the output of each unit. In order to verify the effectiveness of the proposed model and algorithm, simulation experiments are carried out on the typical operating scenarios of the IIES in summer and winter, and the results are compared with those of the other five optimization algorithms. Experimental results show that the proposed algorithm has a good global convergence performance, and the computational cost is lower than the other five optimization algorithms.

Long-term Climate Compatible Growth for India: Modeling Low-carbon Pathways and Policies for India’s Power, Industry, and Transport Sectors

This study is grounded in the concept of carbon budgets and determines India’s fair share of the global carbon budget using four budget allocation approaches. Four Indian research groups then use different energy-economy models to distribute these budgets across time and sectors to determine decadal milestones for India’s key energy sectors, viz. power, industry, and transport.

Making the factors underlying the implicit discount rate tangible

Leading energy-economic models used for policy assessment employ implicit discount rates (IDRs) to model household energy efficiency technology adoption, typically assuming identical IDRs across households and countries. To help modelers and policy-makers get tangible insights about the IDR, we empirically analyse the relations among key factors underlying the IDR and socio-demographic characteristics based on large-scale demographically representative household surveys in eight EU countries. These factors include behavioral parameters (time preferences, risk aversion, loss aversion, present bias), environmental identity, preferences for adhering to social norms, and access to capital. The findings suggest that these factors vary by country and socio-demographic characteristics. Notably, higher age, higher income and higher education of individuals typically imply a lower IDR, ceteris paribus. Policy-makers may rely on the results to tailor policy interventions towards socio-economic groups. For example, results on time discounting suggest that policies such as rebates or loans should target low-education individuals. Results on risk aversion suggest that policies such as warranties or energy performance contracting should target older and low-education individuals and low-income households. Finally, policies addressing lack of access to capital such as grants and soft loans appear most effective for younger, low-education individuals and single, low-income, non-urban households.

Developing an Integrated Energy–Economy Model Framework for Islands

This paper presents a new energy–economy system modelling approach, developed specifically for energy system planning in non-interconnected islands, aiming for decarbonization. Energy system planning is an essential tool to shape the energy transition to reach carbon neutrality in the medium- and long-term horizon. Islands, as small-scale energy systems, have a limited contribution to the global climate targets, but due to their geographical and natural limitations, they present the potential to become frontrunners in the clean energy transition, especially regarding the efficient use of resources. The specificities and complexities of geographical islands cannot be adequately covered by the available energy modelling tools and new advanced approaches need to be developed to provide the appropriate support in designing the future decarbonized energy systems at insular level. Our methodological approach follows the adaptation and customization of well-established energy–economy modelling tools towards the development of an integrated island-scale energy–economy system model, capturing energy demand and supply by sector, heating/cooling and mobility requirements, energy efficiency potentials and their complex interactions through energy prices, storage, flexibility services and sectoral integration. By soft-linking the energy and economy system modelling tools through the consistent exchange of model parameters and variables, we developed a fully fledged modelling framework called IntE3-ISL, designed for islands with a horizon up to 2050.

Retracted: Establishment and Evaluation of Energy Consumption, Carbon Emission, and Economic Models of Retreaded Tires Based on Life Cycle Theory

Retracted: Establishment and Evaluation of Energy Consumption, Carbon Emission, and Economic Models of Retreaded Tires Based on Life Cycle Theory

ANALYSIS OF CO2 ABATEMENT COSTS İN TURKEY USING THE TIMES-MACRO ENERGY MODELİNG SYSTEM

Turkey submitted its INDC to the UNFCCC in 2015, through which committed to reach 21% reduction in GHG emissions from the Business-as- Usual scenario by 2030, signed the Paris Agreement in 2016; and ratified in October 2021 after concurring to reach net zero emissions by 2053. It is therefore needed to examine Turkey’s energy sector to understand the leading cause of emissions and elaborate the policy options to reduce emissions. The TIMES-MACRO modeling framework, a hybrid energy system and economic growth model, is employed in this study to evaluate Turkey’s energy sector and CO2 emissions under various emission reduction scenarios together with associated economic implications. The results show that in the absence of scenario constraints, coal dominates the primary energy supply mix, implying that without mitigation measures, emissions will grow rapidly. The abatement costs of emissions vary according to policy, ranging from 45-265 $/ton CO2 by 2050, while the GDP loss range fluctuates between 0.4% and 1.5%.

The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective

In the EU, COVID-19 and associated policy responses led to economy-wide disruptions and shifts in services demand, with considerable energy-system implications. The European Commission's response paved the way towards enhancing climate ambition through the European Green Deal. Understanding the interactions among environmental, social, and economic dimensions in climate action post-COVID thus emerged as a key challenge. This study disaggregates the implications of climate ambition, speed of economic recovery from COVID-19, and behavioural changes due to pandemic-related measures and/or environmental concerns for EU transition dynamics, over the next decade. It soft-links two large-scale energy-economy models, EU-TIMES and NEMESIS, to shed light on opportunities and challenges related to delivering on the EU's 2030 climate targets. Results indicate that half the effort required to reach the updated 55% emissions reduction target should come from electricity decarbonisation, followed by transport. Alongside a post-COVID return to normal, the European Green Deal may lead to increased carbon prices and fossil-fuel rebounds, but these risks may be mitigated by certain behavioural changes, gains from which in transport energy use would outweigh associated consumption increases in the residential sector. Finally, the EU recovery mechanism could deliver about half the required investments needed to deliver on the 2030 ambition.

Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China

Addressing climate change and air pollution goals in conjunction would be efficient and cost-effective. Dealing with these two challenges is a common issue for urban clusters pursuing sustainable development. Expected to become the fourth international first-class bay area, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) takes the lead in exploring a green and low-carbon transition path as a key element of being a pioneering economic reform demonstration zone. Based on an integrated modeling framework that couples an energy-economy model (IMED|CGE), decomposition analysis, and regression methods, the potential contribution of achieving the climate mitigation target to air pollutant reduction in the GBA by 2050 was quantified. The results showed that the transport sector has the most significant potential for carbon reduction. Energy intensity and structural transformations are the main contributors to reducing carbon emissions, with the latter becoming increasingly important over time. Climate policy can effectively reduce air pollutant emissions; however, this effect varies for different pollutants and sectors. Based on the assessment of the synergy index and cost of abatement, sectors with priority for synergic governance were identified. The regression results indicated that the carbon shadow price would be significantly more effective in reducing air pollutant emissions in the post-2030 period than before 2030, except for SO2 and NH3, partially because of the existing actions that cause the synergistic effects to decline. In addition, end-of-pipe removal measures still play a relatively significant role in reducing air pollutants, particularly VOC, NH3, and primary PM2.5. Thus, the findings suggest that priority should be given to sectors with huge synergistic benefits, such as transportation and power generation while paying attention to possible trade-offs.

Comparison of macroeconomic developments in ten scenarios of energy system transformation in Germany: National and regional results

BackgroundDifferent strategies have been proposed for transforming the energy system in Germany. To evaluate their sustainability, it is necessary to analyze their macroeconomic and distributional effects. An approach to do this analysis in an integrated consistent framework is presented here.MethodsComparing ten energy transition scenarios with emission reduction targets by 2050 of 80% or 95%, respectively, allows evaluating a broad range of energy system transformation strategies with respect to the future technology and energy carrier mix. For this purpose, an energy system model and a macroeconometric model are combined, thus re-modeling the unified scenarios. An important extension of the model was concerned with the integration of synthetic fuels into the energy-economy model. One focus besides the overall macroeconomic assessment is the regional analysis. For this purpose, own assumptions on the regional distribution of the expansion of renewable energies were developed.ResultsThe effects on gross domestic product (GDP) and employment are similar on average from 2030 to 2050 across the scenarios, with most of the more ambitious scenarios showing slightly higher values for the socioeconomic variables. Employment in the construction sector shows the largest effects in most scenarios, while in the energy sector employment is lower in scenarios with high energy imports. At the regional level, the differences between scenarios are larger than at the national level. There is no clear or stable regional pattern of relative loss and profit from the very ambitious transformation, as not only renewable energy expansion varies, and hydrogen strategies enter the scene approaching 2050.ConclusionsFrom the relatively small differences between the scenarios, it can be concluded that, from a macroeconomic perspective, it is not decisive for the overall economy which (supply side) strategy is chosen for the transformation of the energy system. More effort needs to be put into improving assumptions and modeling approaches related to strategies for achieving the final 20% CO2 reduction, for example the increasing use of hydrogen.

Empirically grounded technology forecasts and the energy transition

<h2>Summary</h2> Rapidly decarbonizing the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Most energy-economy models have historically underestimated deployment rates for renewable energy technologies and overestimated their costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. Here, we use an approach based on probabilistic cost forecasting methods that have been statistically validated by backtesting on more than 50 technologies. We generate probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers, conditional on deployment. We use these methods to estimate future energy system costs and explore how technology cost uncertainty propagates through to system costs in three different scenarios. Compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars—even without accounting for climate damages or co-benefits of climate policy.