Marginal Emissions Research Articles

A Quantitative Method of Carbon Emission Reduction for Electrochemical Energy Storage Based on the Clean Development Mechanism

Electrochemical energy storage (EES) plays a crucial role in reducing the curtailed power from wind and solar PV power (WSP) generation and enhancing the decarbonization effects of power systems. However, research on quantifying the carbon emission reduction effects of EES methods in the engineering field is still insufficient, which constrains decision-makers from making intuitive assessments of the decarbonization effects of energy storage. Therefore, drawing on the principles of the clean development mechanism (CDM), this paper proposes a method for quantifying the carbon emission reductions of a standalone EES station. Firstly, based on the design principles of building marginal emission factor in the CDM, a method for calculating the BM weight of WSP is proposed. Secondly, three quantification methods for the carbon emission reductions of EES are presented based on the complexity of the calculations. Lastly, to analyze the impacts of different operational conditions and calculation methods on the carbon emission reduction of energy storage systems, a dispatch model is constructed for various operational scenarios. The results of the case study indicate that different calculation methods yield varying results in terms of the carbon emission reductions of energy storage systems, with the sharply value method yielding the smallest reduction and the output curve method yielding the largest reduction. Additionally, when considering the losses in the state of charge (SOC) of an energy storage system and reducing the overall output fluctuations of WSP-EES, the carbon emission reduction potential of the energy storage will decrease. This study establishes a theoretical basis for quantifying the carbon emission reductions of standalone electrochemical energy storage systems, aiding decision-makers in gaining a deeper understanding of the role of electrochemical energy storage in carbon reduction and operational value.

Beyond the average: Emissions from electricity and the implications of decarbonization policies in Guangdong

Various policy interventions have been proposed to decarbonize the Chinese economy through the electricity sector. This study is the first to use generation-by-plant data to analyze the emissions from the electricity sector in a Chinese region and evaluate the environmental impacts of various policy interventions. Using regression techniques, the average emission factor, marginal emission factor, and marginal displacement factor, are estimated for different pollutants of Guangdong, the most affluent and populous province in China. Results reveal a decline in average emission factors over the sample period of 2017–2021, owing to the expansion of renewable energy. This study also finds a less pronounced disparity between marginal emission factors and average emission factors compared with that in developed countries, probably due to the prevalence of coal-burning plants and lower renewable energy penetration in Guangdong. Using the estimated emission factors, this study also examines the environmental effect of time-of-use tariffs, indirect emissions of electric vehicles, and carbon displacement effect of wind energy. The conclusions reveals that the time-of-use tariffs moderately reduced CO2 emissions, and the extensive adoption of electric vehicles and wind energy substantially reduces carbon emissions, indicating that decarbonizing the economy needs the coordination of various policies.

Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets

Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.

The Impacts on Regional Development and “Resource Curse” by Energy Substitution Policy: Verification from China

With the increasing “resource curse” phenomena relating to energy resources in China, research concerning energy substitution policies has become more meaningful. In this paper, a dynamic CGE model was built to evaluate the impacts of energy substitution policies on regional growth and the “resource curse”. The results show the following: (1) Compared to other regions, an energy substitution policy exerts a more considerable influence on economic growth in regions with high “resource curse”. (2) The changes in carbon emissions in regions with no or low “resource curse” are modest. The primary factor contributing to the rapid decline might be energy structure adjustments. As the intensity of substitution varies, the regional disparities in marginal emission reduction costs are expected to increase. (3) Energy substitution policies reduce the severity of high “resource curse” regions. As the intensity of energy substitution increases, the extent of “resource curse” regions decreases accordingly. Some suggestions are given as follows: (1) Further promote energy substitution and speed up the transformation of energy production and consumption modes in China. (2) Accelerate the energy transformation in “resource curse” regions to promote regional sustainable development. (3) Improve energy substitution policies in transportation, industry and other fields.

Optimizing sustainable development in arid river basins: A multi-objective approach to balancing water, energy, economy, carbon and ecology nexus

The ongoing water crisis poses significant threats to the socioeconomic sustainability and ecological security of arid and semi-arid river basins. Achieving Sustainable Development Goals (SDGs) within a complex socio-ecological nexus requires effective and balanced resource management. However, due to the intricate interactions between human societies and environmental systems, the tradeoffs and synergies of different SDGs remain unclear, posing a substantial challenge for collaborative management of natural resources. Here we introduce a gray fractional multi-objective optimization (GFMOP) model to balance multi-dimensional SDGs through a novel water–energy–economy–carbon–ecology nexus perspective. The model was applied to a typical arid river basin in Northwest China, where thirty-two scenarios were explored, considering factors such as shared socioeconomic pathways, carbon removal rates, water conveyance efficiencies, and ecological requirements. The results reveal a strong tradeoff between marginal benefit and carbon emission intensity, indicating that improving the economic efficiency of water use can simultaneously reduce emissions and protect the environment. Given the immense power generation potential, wind power development should be prioritized in the future, with its share in the energy structure projected to increase to 23.3% by 2060. Furthermore, promoting carbon capture technologies and expanding grassland coverage are recommended to achieve regional carbon neutrality, contributing 39.5% and 49.1% to carbon absorption during 2021–2060, respectively. Compared with traditional single-objective models, GFMOP demonstrates a superiority in uncovering interrelationships among multiple SDGs and identifying compromised alternatives within the compound socio-ecological nexus. The model also provides detailed strategies for resource allocation and pollutant control, offering valuable guidance to policymakers and stakeholders in pursuing sustainable and harmonious watershed management.

Validating locational marginal emissions models with wind generation

Validating locational marginal emissions models with wind generation

The marginal generation and emissions impacts of purchased hydropower: Evidence from the Colorado River Storage Project

As the electric grid continues to modernize, utilities and regulators are continuing to emphasize low-emission, low-cost resources. While wind, solar, and storage are capacity additive targets, marketed hydropower continues to be an attractive source of reliable power for utilities. These utilities rely on hydropower as a cheaper and cleaner alternative to traditional fossil fuel generation. However, hydropower production is subject to continuously evolving ecological and regulatory constraints. Understanding the economic benefits of hydropower purchases to utilities and their customers is useful in the discourse surrounding management of hydroelectric dams. To date, there has been no work that directly examines the fossil fuel generation, emissions, and regional air quality impacts associated with hydropower purchases. In this study, we address this paucity of research by conducting a case study of a non-profit power wholesaler engaged in long-term hydropower purchase agreements in the western United States. We collect hourly generation, stack-level emissions (CO2, SO2, and NOX), and regional ground-level ozone data across a five-year period to assess how purchases of marketed hydropower effect these outcomes. We quantify the average hourly offsets of purchased hydropower, solar, and wind generation. We find that most of the purchased hydropower is passed through to other utility customers, but that overall, significant reductions in fossil fuel generation (particularly coal), emissions, and regional ozone levels occur. We conclude by approximating conservative, lower-bound health incidence benefits associated with marketed hydropower purchases. Our results are useful to policymakers, utilities, and other stakeholders for better understanding the potential benefits of marketed hydropower.

A real-time estimation method of carbon emission for cement corporations based on load identification

Abstract Accurate carbon emission estimation is the basis for assessing the carbon emissions of various regions and industries and formulating reasonable and feasible emission reduction programs. This paper presents a generalized real-time corporate carbon footprint measurement method. The total carbon emissions are categorized into direct emissions and indirect emissions. For the former, a load identification method based on CNN-BLSTM is proposed to monitor the state of the devices, and the carbon emissions are directly calculated in combination with the carbon emission intensity of the devices; for the latter, accurate estimation of carbon emissions is achieved through the application of marginal carbon emission factor. By utilizing this analytical model, the estimated carbon emissions are more accurate than those of other methods, which can better grasp the carbon emissions of enterprises and promote energy saving and emission reduction.

Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050.

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Comparative techno-economic assessment of methanol production via directly and indirectly electrified biomass gasification routes

The sustainable production of chemicals and fuels is a major challenge in order to reduce greenhouse gas emissions and reduce the usage of fossil resources. Biomass gasification with a subsequent conversion of the syngas to methanol is an established route. The direct (electrical resistance heating) or indirect (H2 production via water electrolysis and addition to process) electrification of this route with utilization of renewably produced electricity can improve the effective utilization of biomass which is a scarce resource. Starting with a base case (Biomass-to-MeOH), 10 scenarios with different types and points of electrification are modelled in Aspen Plus to generate mass and energy balances. Based on the generated data, the processes are compared in terms of efficiency, economics, and CO2 emissions.The electrification ratios range from 0.12 for the Biomass-to-MeOH process to 1.31. Electrification can increase the carbon efficiency from 43 % for the Biomass-to-MeOH process to up to 95 % while the energy efficiency ranges from 54.6 % to 60.3 % with 54.7 % for the Biomass-to-MeOH process. The levelized cost of methanol ranges from 642 to 795 €/t. Lowest LCOM are reached for direct electrification of the gasifier or reformer. A flexible operation of the electrified process shows a higher sensitivity on the levelized cost for scenarios with high degree of electrification. The marginal emission factor for the used electricity ranges from 1761 to 352 g CO2/kWh to reach equal emissions than the fossil equivalent. Sustainable methanol can be produced with the electrified processes. Especially direct electrification can improve the efficiency and lower cost in comparison to indirect electrification. A combination of both types of electrification is advised if a high product yield and carbon efficiency are desired.

Holistic analysis of cropping diversity and intensity implications for productive, environmental, and nutritional performance of smallholder farms in Bihar, India

The agricultural productivity and sustainability in Eastern Gangetic Plain Zones of India are threatened because of the inefficiency of current production practices, shortage of resources, and socioeconomic constraints. We hypothesized the potential impact of intensified cereal systems with mung bean as a third crop within the annual cropping cycle. We assessed economic, social, and environmental indicators for intensified and current cropping system management practiced by different farm types in the region using the FarmDESIGN model. Building on a farm typology constructed for the region in our past research, we used five types of farmers: part-time (PT), well-endowed (WE), small-scale (SS) crop and livestock mix, medium-scale (MS), and resource-poor farmers (RP) in this study. The performance indicators of the 229 original cropping systems cultivated within the 43 farms varied strongly in the eight performance indicators. This variability of cropping systems performance within the farm types resulted in the absence of significant differences between the types. Compared to the original cropping systems, the intensified cropping systems with mung bean not only performed high in dietary energy (DE) production and organic matter (OM) inputs into the soil but also had high application rates of biocides and minimized losses of nitrogen (N). The intervention systems were low in labor requirement and scored at an intermediate level for crop gross margin, water use, and greenhouse gas (GHG) emissions. The ranges of areas of maize- and rice-based systems that could be replaced by intensified systems were largest for the WE and RP farm types. This was reflected in large ranges of change in the performance indicators, but no significant differences in response were found between the farm types. The intensification of maize- and rice-based systems with the proposed intervention cropping systems involving mung bean would result in increased profitability, higher DE yield, and lower requirements for labor and water as the proportion of the farms being converted increases. However, the use of biocides would increase, while the intervention cropping systems would have no significant effect on OM input, GHG emissions, and soil N losses.

Advancing cost-optimal residential decarbonisation pathways: An examination of heat pumps and thermal efficiency

In Ireland, residential energy policy has been influenced by thermal inefficiency, high electricity prices and energy poverty. These factors have driven the fabric-first approach, where buildings must undergo extensive thermal retrofitting to qualify for heat pump subsidies. The cost-effectiveness of this approach has not been properly scrutinised to date. This paper addresses this knowledge gap by exploring the cost-effectiveness of the fabric-first approach and other decarbonisation strategies for the residential sector and broader energy system. The study develops scenarios by modifying the Heat Loss Indicator (HLI) threshold, which evaluates the fabric and ventilation heat loss per unit of floor area and is used to determine heat pump subsidy eligibility. The paper also considers scenarios that permit installing sub-optimal performing heat pumps that do not achieve peak performance levels in dwellings with higher HLIs. The analysis uses the TIMES-Ireland Model (TIM), a model of the whole energy system that reflects the interdependence of mitigation pathways across different energy sectors. Our study shows that Ireland's fabric-first approach requires 22 times more thermal retrofits to meet climate targets than alternative pathways. The alternative pathways based on the latest heat pump performance data are more cost-effective, showing that higher heat pump HLI thresholds can reduce the average sectoral marginal CO2 emission price by 60% compared to the current fabric-first approach. The cost savings indicate that shifting to an HLI threshold of 2.3 W/K/m2 is close to optimal, while cost savings are minimal when moving from an HLI of 2.3 to 3 W/K/m2. Furthermore, electricity prices and household awareness are essential to the cost-optimal transition to residential heat pumps.

Call for multi-policy approach: Synergistic effects of emissions trading scheme and energy efficiency policies

Limited research exists on the synergistic effects of carbon emissions trading and energy efficiency policies despite their significance in achieving global carbon neutrality objectives. This study examines the synergistic effects of carbon emissions trading and energy efficiency policies on aspects of the environment, energy, and economy. Results show that the synergistic effect leads to an additional reduction of 1.2% in carbon emissions, along with a decrease of 4.2% in economic losses. Despite challenges like increased energy external dependency and carbon leakage, the synergistic effect shows a positive externality between policies, reducing the carbon intensity and marginal emission mitigation costs. Furthermore, these synergistic effects yield positive consequences for social welfare, particularly benefiting rural households and fostering equitable distribution of carbon mitigation benefits across societal groups. These findings underscore the importance of considering policy synergies between carbon emissions trading and energy efficiency policies to ensure the total effect of climate change mitigation strategies.

Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals

Electricity pricing can be used to shift the timing of electricity demand, but the choice of price signals is highly constrained. Consumer rates are updated every few years and limited to simple daily profiles, yet must capture the complex dynamics of a changing electricity system. Emission factors (EFs) were developed as an evaluation tool, but are increasingly used as demand response (DR) signals. Given these constraints, can they be effective? We evaluate the emissions impact of EF-based electricity rates with and without supply-side emissions pricing. We study controlled electric vehicle (EV) charging in the Western U.S. up to 2037 by coupling an electricity system dispatch model and a data-driven EV charging model. We compare average and short-run marginal EFs with a new medium-run marginal EF that better matches the timeline of electricity rate updates. We find that a stable supply-side signal makes DR more valuable: DR reduces emissions by up to 6% with supply-side carbon pricing or just 2% without it. Medium-run marginal EFs yield the most consistent emission reductions, but constraints on charging flexibility limit their impact. We recommend policymakers base rates for DR on medium-run marginal emission factors and implement supply-side carbon pricing to facilitate greater emission reductions.

A comparative prospective life cycle assessment of coal-fired power plants in the US with MEA/MOF-based carbon capture

The adoption of carbon capture technology in coal-fired power plants is expected to play a pivotal role in the energy transition. This study conducted consequential life cycle assessments (CLCAs) of coal-fired power generation in the United States using policy-level accounting. Monoethanolamine (MEA)-based and Mg-MOF-74-based carbon capture have been introduced, with a comparative analysis conducted on the emissions reduction potential of these two materials through their respective mechanisms of absorption and adsorption. The results indicate that carbon capture based on MEA or Mg-MOF-74 can significantly reduce emissions from coal-fired power generation, decreasing from 779.5 Mt CO2e to 50.1 Mt CO2e and 61.1 Mt CO2e in 2050, respectively. The introduction of ultra-supercritical power plants and carbon capture reduced direct emissions from 92% to 51%. MEA outperforms Mg-MOF-74 slightly, with lower emissions due to solvents and cleaning processes. Deviations in Mg-MOF-74's adsorption capacity and degradation rate could lead to 4%–6% model outcome variations. It is also concluded that the stability of MEA's marginal emissions depends on a steady expansion of existing production capacity, while the marginal emissions of Mg-MOF-74 are anticipated to remain unchanged. This study emphasizes carbon capture's potential but stresses the need for prompt implementation and comprehensive assessments before deployment decisions.

Study on real-time estimation of carbon emissions based on the non-intrusive power load monitoring

Monitoring carbon emissions, as well as evaluating and controlling the level of carbon emissions, are important prerequisites for combating climate change and promoting sustainable development. In this paper, real-time estimation of carbon emissions based on non-intrusive electricity load monitoring is investigated, existing machine learning approaches and related issues of load identification and marginal carbon emission factors are elucidated, and existing carbon emission monitoring approaches are analyzed and summarized. The methods of real-time carbon emission measurement are summarized, and their development is anticipated. Most of the existing methods for measuring and analyzing carbon emissions in power systems are unable to meet the development needs of real-time carbon emission calculation and have the disadvantages of higher installation and maintenance costs and poorer economy of monitoring equipment. Therefore, in this paper, a real-time carbon emission monitoring scheme based on deep learning is used to estimate carbon emissions by dividing them into direct carbon emissions and indirect carbon emissions.

Is District Heating a cost-effective solution to decarbonise Irish buildings?

Achieving decarbonisation of the heating sector is a major challenge, especially in Ireland, which has the lowest proportion of renewable heat in Europe. In economically pursuing ambitious and equitable climate targets, Ireland’s Climate Action Plan 2023 set a goal of reaching 2.5 TWh of district heating by 2030, starting from a limited existing capacity. This pioneering study explores supply sources and heat density demand cohorts to develop a model to understand district heating in a low-deployment region, using Ireland as a case study. We also conduct a sensitivity analysis considering different connection rates.Our research demonstrates that district heating is pivotal in achieving optimal decarbonisation pathways. Incorporating district heating reduces marginal CO2 emission prices by 13%–25%. Additionally, with feasible growth limits applied, total savings for the energy system are anticipated to reach €17.2 billion by 2050. This reduction of emissions in the building sector, facilitated by district heating, allows for greater emissions allowances in other sectors and reduces reliance on electricity while meeting climate targets. These insights highlight the need for spatial energy policies and deepen our understanding of integrating district heating within an energy system optimisation model.

The impact of heavy truck electrification on greenhouse gas emissions in Ontario, Canada

In this work, the new energy demand and greenhouse gas emissions that would be associated with heavy truck electrification in Ontario is evaluated. A new equation is derived to calculate the pollution-producing electricity generation based on the breakdown of the Ontario generation mosaic. Using this as a basis, for 4 scenarios of 5 %, 25 %, 50 % and 75 % of heavy-duty truck electrification, the Marginal Emission Factor (MEF) is calculated. This evaluation suggests that Ontario's peak demand in 2040 for the least and the most heavy-duty truck electrification scenarios can be up to 27.5 GW and 30.4 GW, respectively, compared to a baseline of 26.8 GW when assuming no heavy truck electrification condition. It is also forecasted that if the extra demand for electric trucks is supplied by clean renewable resources, the GHG emission reduction for 2040 can be as high as 0.9 MT, 4.3 MT, 8.6 MT and 12.9 MT GHG, respectively.

Medium and Long-term Carbon Emission Projections and Emission Reduction Potential Analysis of the Lingang Special Area Based on the LEAP Model

Based on the existing statistical data of the Lingang Special Area in Shanghai and considering its future socio-economic development， industrial structure， and technological development， a LEAP-Lingang model was developed to analyze the evolution trends of energy demand and carbon emissions under the baseline scenario， low-carbon scenario， and enhanced low-carbon scenario. To enhance the prediction accuracy of the model， the Logistic population growth model was used to predict future population data， and the learning curve model was used to simulate the cost evolution trend of related carbon reduction technologies. In addition， an economic evaluation model for carbon reduction technologies was developed， and the economic costs and emission reduction potential of typical carbon reduction technologies were evaluated by drawing a marginal emission reduction cost curve. The results showed that under the enhanced low-carbon scenario， the renewable energy accounted for 69% of the primary energy consumption， and the electric energy accounted for 91% of the terminal energy demand in 2060. The Lingang Special Area could achieve carbon peak by 2030， and the carbon emissions in 2060 were predicted to decrease by 94% compared to that in the baseline scenario. In terms of contribution to emission reduction， clean energy substitution， industrial structure optimization， and terminal energy efficiency improvement played a key role in reducing carbon emissions near the port. In the medium term （until 2035）， they were predicted to contribute 35.1%， 27.3%， and 16.2% of carbon emissions， respectively， and in the long term （until 2060）， they should contribute 50.6%， 8.75%， and 7.7% of carbon emissions， respectively. Regarding specific carbon reduction technologies， hydrogen power generation； water electrolysis for hydrogen； and carbon capture， utilization， and storage （CCUS） technology were of great significance for achieving net-zero emissions， but the costs of emission reduction were relatively high. The research results can provide ideas and references for the low-carbon and green development of the Lingang Special Area and related areas.

Ensuring greenhouse gas reductions from electric vehicles compared to hybrid gasoline vehicles requires a cleaner U.S. electricity grid

Emissions from electric vehicles depend on when they are charged and which power plants meet the electricity demand. We introduce a new metric, the critical emissions factors (CEFs), as the emissions intensity of electricity that needs to be achieved when charging to ensure electric vehicles achieve lifecycle greenhouse gas emissions parity with some of the most efficient gasoline hybrid vehicles across the United States. We use a consequential framework, consider 2018 as our reference year, and account for the effects of temperature and drive cycle on vehicle efficiency to account for regional climate and use conditions. We find that the Nissan Leaf and Chevy Bolt battery electric vehicles reduce lifecycle emissions relative to Toyota Prius and Honda Accord gasoline hybrids in most of the United States. However, in rural counties of the Midwest and the South, power grid marginal emissions reductions of up to 208 gCO2/kWh are still needed for these electric vehicles to have lower lifecycle emissions than gasoline hybrids. Except for the Northeast and Florida, the longer-range Tesla Model S battery-electric luxury sedan has higher emissions than the hybrids across the U.S., and the emissions intensity of the grid would need to decrease by up to 342 gCO2/kWh in some locations for it to achieve carbon parity with hybrid gasoline vehicles. Finally, we conclude that coal retirements and stricter standards on fossil fuel generators are more effective in the medium term at reducing consequential electric vehicle emissions than expansion of renewable capacity.