Assessing the Potential Health Impacts Associated with Efficiency Standards-Induced Changes to Air Quality at a Regional Level (Air Quality Index Analysis)
Abstract
This Project investigates the health implications of efficiency standards-induced changes to air quality within the framework of sustainable development goals. Drawing on interdisciplinary insights, the study examines energy production’s environmental and social impacts, emphasizing the role of efficiency standards in mitigating emissions. The research aims to quantify the health effects of efficiency standards in the Northeast of the United States through data-driven analyses and modeling, including air quality index assessments and energy load profile studies. By informing evidence-based policymaking, the study contributes to holistic approaches to energy transition, aligning with the broader agenda of sustainable development.
Table of Contents
V. Methods, Data Collection and Analysis.
List of acronyms
SDGs Sustainable Development Goals
AQI Air Quality Index
DOE Department of Energy
LBNL Lawrence Berkeley National Laboratory
NREL National Renewable Energy Laboratory
TSL Trial Standard Levels
BTUs British Thermal Units
RAC Room Air Conditioners
kWh Kilowatt-hour
EPA Environmental Protection Agency
GHG Greenhouse Gases
CO2 Carbon Dioxide
NOX Nitrogen Oxides
SOx Sulfur Dioxides
Hg Mercury
CH4 Methane
N2O Nitrous Oxide
PM2.5 and PM10 Particulate matter
I. Background
All United Nations Member States adopted the 2030 Agenda for Sustainable Development in 2015, which provides a shared blueprint for peace and prosperity for people and the planet, both now and in the future. The 17 Sustainable Development Goals (SDGs) are an urgent call to action by all developed and developing countries in an international partnership. The SDGs recognize that ending poverty and other global issues must pair with strategies that improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests.[1] A better understanding of the environmental and social impacts, specifically of energy production and use, corresponds to the following Sustainable Development Goals:
● Goal 3: Good Health and Well-being
● Goal 5: Gender Equality, Goal 6: Clean Water and Sanitation
● Goal 8: Decent Work and Economic Growth
● Goal 9: Industry Innovation and Infrastructure
● Goal 10: Reduce Inequalities
● Goal 12: Responsible Consumption and Production
● Goal 13: Climate Action
The transit to sustainability in energy production and use
The world is going through a process of electrification in all sectors: Transportation, residential, and commercial. This transformation aims to reduce fossil fuel energy production and lower greenhouse gas emissions from electricity production. Industries such as construction, iron and steel, and chemical production are adopting new methods, electrifying their production processes to the extent possible, and reconfiguring their supply chains to lower their carbon footprint.
The current energy grids worldwide are aging systems initially designed to run on fossil fuels. Most were designed to supply electricity for just decades. Today, this transformation process forces decision-makers to plan to provide energy in very short time horizons. This will require more resources, time to implement these changes, and political support for new and green infrastructure in general.
Although electric demand grew only about 5% during the past decade[2], the energy transition is driving a rapid increase in energy demand. Some significant aspects of the transition relate to preemptively responding to projected energy demand while assuring that such production will not create new forms of exploitation or unfair distribution of externality costs. In the energy transition context, electricity demand is projected to rise by between 14 and 19% by 2030 and between 27 and 39% by 2035.[3] This new demand will have various implications, from reconfiguring supply chains to building new green-energy electric plants to the politics of who cashes in the benefits and who bears the environmental and social costs of energy production.
Energy policy is entangled with economic and political interests. Facing global challenges also requires long-term planning and policymaking. It is imperative to have good data to show evidence of the environmental and social costs fronted by communities at the forefront of energy production. These social costs represent a burden, and having sound data on the costs they pay for the nearby electric plants allows communities to hold decision-makers accountable for policies enacted on their behalf that may go against their well-being and their communities. Costs and benefits may be explicitly financial, such as a surcharge for clean energy on an electric bill, or positive or negative externalities, such as changes to local pollution levels.
One of the ways energy load profiles have been changing is with more energy-efficient appliances such as ENERGY STAR certification, using smart technologies, incorporating energy-saving modes, and improved insulation. One remarkable step in reducing energy demand from energy-using consumers, commercial, and industrial products has been implementing Energy Star efficiency standards as the gold standard for appliance and equipment certification. According to Energy5, energy efficiency standards have produced cost savings for consumers in their energy bills, created environmental benefits by reducing carbon footprint, and constrained producers to make longer-lasting, better-performing appliances.[4]
This Capstone project aims to build a model to measure energy production's environmental and social impacts in the United States through changing energy load profiles. Exploring how energy production impacts communities around energy plants will be necessary to better understand how to enable a just energy transition. Moreover, it aspires to answer the following question: How can the emissions-related health impacts of efficiency standards be estimated?
I. Literature Review
a) Air Pollution Health Effects
Air pollution is a significant environmental risk to people’s health. Mortality rates have been closely linked to air pollution levels, with adverse health effects associated with particulate matter such as PM2.5 and PM10. Particulate matter penetrates the body through the lungs, and prolonged exposure is associated with respiratory and cardiovascular diseases, reproductive and nervous system dysfunctions, and various types of cancers. It is documented that these high levels of air pollution may jeopardize the well-being and development of a community at large by affecting vulnerable populations the most.
Studies have shown the link between air pollution and developmental damage to children’s brain structures (Burnor et al., 2021) and in adolescents (Cotter et al., 2023). Prolonged exposure to air pollution has also been associated with forms of cancer - specifically documented is the mounting evidence associating it with breast cancer (White, 2021). A study conducted by Gabet et al. (2021) concluded that “a 10 μg/m3 increase in nitrogen dioxide (NO2), a marker of exposure to traffic, is associated with a 3% higher risk of breast cancer”. Other studies point out how the type of particulate matter emitted from energy plants (PM2.5) is associated with an increased risk of dementia (Shaffer et al., 2021). Air pollution from fossil fuel combustion is associated with negative health impacts.[5] People who reside closest to power plants will have a higher risk of prolonged exposure. Conversely, some countries have been able to reduce the burden of disease from stroke by lowering pollution levels[6]. Unfortunately, a class and racial component is associated with this higher-risk category, exacerbating the effects of poverty (Thind et al., 2019).
b) Burning Fossil Fuels for Energy Production and Pollution
According to the United States Environmental Protection Agency (EPA), climate change and air pollution are interconnected, with burning fossil fuels contributing to carbon pollution and impacting air quality[7]. Undeniably, communities that are most vulnerable to climate change face compounded impacts from the pollution derived from energy plants using fossil fuels:
a) The direct health effects of pollution and
b) The effects derived from climate change.
Thus, improving air quality to meet the World Health Organization-recommended levels could prevent many premature deaths caused by exposure to fine particles[8]. Clean air is crucial for human health, vegetation, and crop health, enhancing people's enjoyment of scenic areas[9]. Poor air quality has been linked to various adverse health effects, emphasizing the importance of implementing measures to improve air quality[10].
As the world continues to shift toward electrifying energy systems and increasing energy demand, communities worldwide will face increased health-risk exposure from energy generation. It is essential to highlight the need to leverage big data to understand these impacts better and recommend and implement sound public policy[11]. One example of a broad policy effort is the zero-pollution action plan by the European Union, which aims to reduce premature deaths due to exposure to fine particles.
c) Efficiency Standards and Energy Demand
Improving efficiency standards in all sectors (transportation, industry, and energy production) is paramount to keeping energy demand as low as technologically possible. For example, Tanako Tanaka (2011) finds that enhancing energy efficiency within the industrial sector is paramount for advancing energy security, environmental sustainability, and economic prosperity. Tanaka finds examples of governments worldwide that have introduced many policies and initiatives to bolster energy efficiency in manufacturing industries. These efforts prioritize cost-effective reductions in energy consumption and greenhouse gas emissions. The effectiveness of such measures may be attributable to their capacity to efficiently diminish energy usage and CO2 emissions, their straightforwardness in policy formulation and implementation, and their broader societal benefits.
In 2004, Gillingham et al. wrote about the importance of demand-side energy efficiency policies, with appliance standards as a primary tool to decrease demand for energy and emissions related to energy production. The authors saw this approach as having a lasting presence. In a paper by Saunders et al. (2021), the authors conduct a critical assessment and review covering four decades and a vast geography (10 nations) of energy efficiency standards implementation. They find that energy efficiency programs have reduced the energy used per output unit while improving welfare and reducing emissions. They show that implementing energy efficient standards has resulted in new energy-saving technologies (such as better lighting), lowering energy costs, and lowering consumption (and thus production emissions) while encouraging technology adoption.
d) Measuring Health Effects from Energy Production
One approach to estimate the emissions-related health impacts of efficiency standards is to quantify the reduction in energy consumption due to the standards and then calculate the associated reduction in emissions. These emissions reductions can be linked to health impacts using established concentration-response functions. Gillingham et al. (2006) provide evidence that energy efficiency standards have led to improvements in energy efficiency, which in turn can reduce emissions of greenhouse gasses and air pollutants, thus having a positive impact on health. Fann et al. (2013) focus on the health impacts of fine particulate air pollution from electricity generation in the US. Still, they do not specifically address estimating emissions-related health impacts of efficiency standards. However, both works provide insights into quantifying emissions reductions due to energy efficiency policies and the associated health impacts of air pollution, which can be applied to estimate the emissions-related health impacts of efficiency standards.
e) Efficiency Standards and Their Purpose in Different Sectors
Efficiency standards help regulate the energy performance of products and processes, reduce energy consumption, and improve energy efficiency. In the industrial sector, equipment efficiency, process efficiency, and energy management regulations are implemented to improve energy efficiency (Tanka, 2011). These standards attempt to influence total process efficiency and process configurations in industrial sectors, encouraging plants and firms to employ energy management processes by setting standards for energy management (ibid). In the residential sector, minimum energy efficiency standards for appliances are economically efficient when they correct a market failure, as they tend to increase the efficiency of products available to consumers (Spurlock & Fujita, 2022).
Efficiency standards are used in the transportation sector, where Corporate Average Fuel Economy (CAFE) Standards regulate the energy efficiency of vehicles (Gillingham et al., 2004). These standards are periodically updated to improve energy efficiency and reduce energy consumption (ibid).
f) Efficiency Standards and Trends in Emissions
There are clear trends in pollutant emissions before and after implementing efficiency standards. For instance, global sulfur dioxide (SO2 ) emissions decreased in the 1990s by more than 20%, and current energy and air pollution control policies should lead to a further decline (Cofala et al., 2007). Additionally, studies have found that improved energy efficiency in lighting and various appliances strongly correlates with introducing energy efficiency standards without a noticeable cost penalty (Saunders et al., 2021). These findings suggest that implementing efficiency standards has reduced pollutant emissions.
Energy efficiency standards for power generation, manufacturing, and road transport can reduce emissions of (SO2) and nitrogen oxides (NOX), both of which are major air pollutants (Crippa et al., 2016). Implementing appliance standards has induced energy efficiency improvements exceeding 7% for room air conditioners and water heaters (Saunders et al., 2021). This effect indicates that implementing efficiency standards may be associated with improved energy efficiency and reduced energy consumption, which can contribute to lower pollutant emissions.
In the European Union, stricter requirements to tackle air pollution at the source, such as from agriculture, industry, transport, buildings, and energy, are part of the European Commission's plan to address pollution and achieve zero pollution for air, water, and soil (European Commission, 2021). These measures may contribute to improved air quality.
There are variations in the effectiveness of these standards across countries as they may differ due to differences in energy markets, economic environments, business situations, and managerial priorities (Tanaka, 2011). In the United States, the impact of energy efficiency policies on actual energy consumption varies across regions, with at least three-quarters of emission savings being due to energy intensity improvements rather than to decarbonizing energy supply (Saunders et al., 2021)
g) Disparities and Equity Considerations
The structure of the household appliance market can influence the effectiveness of energy efficiency standards. For instance, a theoretical model illustrated that energy efficiency standards have different impacts depending on the structure of the household appliance market, suggesting that appliance standards can be welfare-improving, even for low-income consumers, if producers price discriminate and use energy intensity to help segment consumer demand (Gillingham et al., 2004). Indeed, local contexts and market structures are essential when designing and implementing energy efficiency policies.
The research paper "Equity Implications of Market Structure and Appliance Energy Efficiency Regulation" discusses the equity implications of energy efficiency standards and their impact on consumers, particularly those with lower incomes or renters. The report highlights the need to understand the distributional impacts of these standards and their implications for vulnerable consumers (Spurlock & Fujita, 2022).
Another relevant piece of literature is "Retrospective Examination of Demand-Side Energy Efficiency Policies," which provides insights into energy efficiency policies' environmental and health impacts, including appliance standards. It discusses the potential benefits of these policies for lower-income consumers and the additional benefits from ancillary reductions in air pollution (Gillingham, 2004).
Regarding environmental justice, the study "Fine Particulate Air Pollution from Electricity Generation in the US" examines the health impacts of fine particulate air pollution from electricity generation for each of the seven Regional Transmission Organizations (RTOs). The paper looks at exposure to PM2.5 and health impact disparities by race, income, and geography. The authors find that disparities are larger by race than by income and that most impacts are attributable to coal rather than other fuels (Thind et al., 2019).
III. Discussion
a) Defining Environmental and Social Impact of Energy Production
Modern consumer societies use energy to produce goods and services all along supply chains and life cycle of materials: From the extraction of raw materials to their transformation into viable materials for manufacturing, from the assembly to the transportation, packaging, sale, and disposal of a good. Thus, energy consumption is present in every aspect of goods and services. Generating energy implies significant environmental impacts in extraction, production, transportation, and consumption. For example, power plants are the primary source of CO2 emission in the electrical industry (Akbari-Dibavar et al., 2021). Electric power plants, also known as power stations or generating plants, may use a variety of fuels to generate electricity: Coal, nuclear, oil, gas, hydroelectric, and renewable sources. Fossil fuel generation contributes strongly to CO2 emissions, but all forms of generation lead to environmental and social externalities.
Impacts are not limited to the generation of greenhouse gasses, which in turn cause climate change. They encompass air, water, thermal pollution, and solid waste disposal. In the case of nuclear energy, the externalities are much more complex, such as the generation of radioactive waste material, extensive land use, and water and soil pollution, which have a much longer time horizon. Social impacts are the effects experienced by people in communities surrounding power plants due to emissions and waste during the energy production process. This research is focused on air quality effects from energy production and its impact on those communities near generation plants. The main reason why it uses air quality is because there is data and existing models available that link air quality differentials to health effects as explored in the literature review.
b) Indicators Used for Assessing the Impact of Energy Production
This research uses indicators of air quality, health effects, the number of premature mortalities, and social costs based on the value of a statistical life from the Environmental Protection Agency (EPA)[12] to calculate social costs from pollution levels. The results from the load profile models will then be fed to these health models to predict the health effects of increased energy production.
This research considers different types of appliances because use patterns produce different demand load profiles based on time of day, day of the week, or time of year. For example, cooling equipment is in high demand during the daytime hours of the summer, while refrigerators are in reasonably constant demand throughout the year. On the electric generation side, certain power plants vary in ability to contribute to the grid at any given time. Renewable resources are most variable and tied to specific weather and timing conditions, while fossil fuel plants can compare quickly and easily increase or decrease electricity output as needed. Thus, changes to electric demand profiles translate to different regional electricity generation sources and thus have different profiles of emissions and air quality. These, in turn, relate to differences in local air particulate matter content and associated health impacts and mortality rates.
The US Department of Energy’s Appliance and Equipment Standards Program ensures that over 60 types of appliances and other energy-using equipment purchased and used in the US meet a minimum efficiency level. Standards are updated periodically to keep pace with technological and market developments. Standard setting is a complex process, supported by a set of analyses designed to allow for the comparison of several alternative levels (Trial Standard Levels, “TSL”) across a wide array of metrics, including estimates of changes to national product shipments, per household lifecycle cost, energy consumption, employment in various sectors, etc. Included in these analyses are high-level estimates of national changes in carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2), mercury (Hg), methane (CH4), and nitrous oxide (N2O).
However, the current set of U.S. minimum energy conservation standard analyses needs to adequately address the potential for human health and mortality changes associated with each TSL and only consider emissions at the national level. The amount and type of emissions associated with electricity production can vary substantially across regions, and those near generators will most strongly feel any emissions-related health impacts.
The broader goal for this project is to develop an emission and mortality modeling process that can be replicated for any appliance such that the effect of selecting a particular minimum efficiency level on emissions-related health impacts (mortality and estimated social costs) could become a potential future decision criterion.
V. Methods, Data Collection and Analysis
The main method used for this analysis was quantitative analysis, which comes from two sources: Metering Data gathered by LBNL researchers in the United States Northeast and the ResStock model datasets from the National Renewable Energy Laboratory (NREL).
One of the central questions of this research project is how to know if an appropriate estimation approach has been identified. To obtain reasonable estimates, criteria for good metrics must be established. First, the principle to follow when estimating the impacts of alternative efficiency levels is to ensure that they fit within the existing set of analyses for each rule and ideally use the same input data as other analyses and the outputs of different analyses.[13] Second, any outside data sources necessary for the Air Quality Index (AQI) analysis should be available to the Efficiency Standards Department at no cost. Last, the approach should be well-documented, transparent, and readily reproducible.
To that end, this research has been using two sources of appliance load data:
1) Residential appliance metering data for Massachusetts and Pennsylvania (2022-2023) collected by researchers at Lawrence Berkeley National Laboratory (LBNL)
2) Modeled load shaped by appliance produced by researchers at the National Renewable Energy Laboratory (NREL).
Appliance efficiency levels have been modeled using the following resources produced by LBNL:
The most recently published efficiency standards, trial standard levels (TSLs), are the potential standard efficiency levels under consideration, compared to a baseline scenario that reflects a no-new standards case. This research is aligned with the rest of the standards-setting analyses by considering the AQI and health impacts of the same set of possible efficiency levels.
Appliance shipments and stock models: The shipments model and projections from the most recently published efficiency standards align with assumptions regarding future appliance use.
Another element incorporated is a third resource from Lawrence Berkeley Laboratory, BILD AQ modeling workflow. It is a recently developed sequence of models that:
Replicate the functioning of the US electric grid, accounting for different electricity sources to determine generator-level emissions of NOx, SOx, and particulate matter,
Localize generation impacts,
Apply the InMap model to the increase (or change) in particulates (pm2.5) to estimate the change in mortality – which will be the main indicator.
This research will use the most recent Room Air Conditioner (RAC) Trial Standard Levels to test the range of impacts produced by InMap. A visual representation of how these pieces fit together is included in Figure 1 below:
Figure 1: Air Quality Model Workflow with Inputs and Outputs
Two approaches are available to reach the desired load profiles: represented by the top left yellow parallel arrows in Figure 1. Ultimately, the model incorporated both avenues to obtain a more robust model that better reflects the range of possible outcomes.
The first approach used the Pennsylvania RAC metering data by generating one full year of power readings for the RAC units available in the homes metered in PA. The original frequency of observations for all units was power in watts per hour at 5-second intervals after correcting for line loss. The first step was to average power readings by the hour, accompanied by the exterior temperature reading associated with each home (also available). Having outside temperature and power readings per hour allows for establishing a correlation between RAC electricity demand and temperature. In addition to these features, each unit's motor type (variable or single-speed unit) and cooling capacity are in British Thermal Units (BTUs).
One issue from this first approach was that the power readings from the metered RAC units did not represent the U.S. as a whole or regions far from PA. Furthermore, there was a small percentage of missing readings for each unit, some with more missing observations than others. However, these missing observations were clustered and sporadic. This could have resulted from a faulty remote sensor, transmission, or malfunction. One way of correcting this was using a predictive regression algorithm to complete the missing data. Another issue was the small sample size, which raised concerns about the statistical power of a model constructed from these data. Nevertheless, the observations can be used for demonstrative purposes since they offer highly granular metering of these RACs, which is expected to represent appliance use in Pennsylvania.
The data also showed time spans without readings, representing periods when the unit was removed from the window and stored for the winter. This surprised researchers in the team from moderate/warm climates, but it makes sense in a region that can see months of snow. This discovery also drove home the importance of not using PA load profiles to represent RAC use in the United States, broadly.
Some of the regression forms tried to fill in missing readings:
a) Hourly power = f(temp)
b) Hourly power = f(temp, hour indicators) = f(temp, hr0, hr1, hr2, hr3… hr23)
c) Hourly power = f(temp, hour, day indicator) = f(temp, hr0, hr1, hr2… hr23, weekday, weekend)
The predictions were from the last iteration c), which the team considered the best-fitted model. Researchers observed that energy consumption was not homogeneous during the day. Rather, there were usage peaks when household dwellers returned home from work or during the weekends when the home dwellers were away. In addition, residents will seldom use their AC at night unless temperatures swell. Thus, the model included a dummy variable for weekends vs. weekdays and hours of the day to capture these nuanced variabilities.
The second avenue of research used was the ResStock model. This is a simulated end-use and energy savings profiles produced by the National Renewable Energy Laboratory (NREL). The NREL datasets provide several options for representing energy use timing and savings in the United States building stock. For this project, the research made use of the “End-Use Load Profiles for the U.S. Building Stock” dataset, which aggregates the nearly 1 million individual simulations into 15-minute resolution load profiles for all major residential and commercial building types and end uses across all climate regions in the United States. This allowed the team to look at Pennsylvania in kilowatt-hour (kWh) power usage in 15-minute intervals (“frequency”). The goal of using these two approaches was to compare data from NREL model (at the same resolution) to the PA RAC metering data. To do so, the data was formatted from the 15-minute interval into one-hour intervals to ensure both datasets share the same time (1-hour intervals) and units (kWh).
NREL Baseline energy load profile Jan-Dec by day of the year
Figure 2 National Renewable Energy Laboratory (NREL). (2021). End-Use Load Profiles for the U.S. Building Stock. https://dx.doi.org/10.25984/1876417
The approach of using NREL’s ResStock model data as a baseline also had many issues. For example, while calibrated and validated, these readings are simulated load profiles (unlike the PA RAC metering data). Another issue is that the class ‘Room AC’ is included in the “cooling capacity” profile,[14] not separately modeled, and cannot look at single vs. variable speed units. This is significant because the motor type is also related to efficiency levels, and these characteristics cannot be identified within ResStock. On the other hand, these motor types are a distinct feature in the Metering Dataset. Using this feature as a point of comparison would have been good as it would provide another dimension for the study. Lastly, the simulated load profiles provide aggregate figures: They come in total for all households and had to be scaled down to a single household profile. In doing so, the data is formatted in national averages, and as discussed previously, because significant differences in RAC unit usage between regions are observed, nuances of idiosyncratic usage patterns may be lost or diluted. On the other hand, there is no missing data with observations for the entire year, which removes the need for additional estimation work.
Shipments and Stock
A second leg of the model is the use of the shipments and stock model to allocate units per balance area in the state of Pennsylvania at the same resolution for both the ResStock calculation and the Metering Data, are represented by the baby blue and purple downward (second) arrow in Figure 1.
The Air Quality Index (AQI) Analysis calculations rely on annual national shipment predictions from the Room Air Conditioners (RAC) Shipments Analysis to obtain RAC stock estimates. The shipments analysis published by the Department of Energy (DOE) projects future shipments of room ACs based on key market drivers. These projections of shipments are the key to calculating the potential effects of standards on national energy use, net present value, and future manufacturer cash flows. The DOE generates shipment projections for each product class[15]. These projections estimate the total number of Room ACs shipped each year during the 30-year analysis period (2026–2055). The shipment figures were used to calculate RACs for the analysis.
Additionally, the model incorporates the Residential Energy Consumption Survey (RECS) to obtain estimates for RAC stock. This survey, conducted by the U.S. Energy Information Administration, estimates residential air conditioning units per state.[16] The number of units from RECS is then used to downscale from national shipments to the Pennsylvania test case. Because the goal is assessing the difference between the base case (no new standards) and different Trial Standard Levels (TSLs) across efficiency levels, there is no need to know the full stock's size or total energy use. This is helpful because there will be no need to make additional assumptions about the age and efficiency of RACs already out in the total stock of AC units before the standard.
Key shipments and stock values the analysis included:
a) By year, number, and energy use of RAC shipped and operated under base case:
Nbase,y, Ebase,y*
b) By year, number, and energy use of RAC shipped and operated under:
TSLn: NTSLn,y, ETSLn,y
While a projection for future years will not be attempted in the initial test case, it may be useful to estimate air quality and health impacts beyond the first year of the standard. The Shipments Model from the RAC rule can be converted to an estimate of annual post-standard-implementation stock using an average lifetime or lifetime distribution. Other analyses for each rulemaking consider time periods of 30 years after the standard becomes effective or the product's expected lifetime. To better align with other analyses, the air quality and health analysis should include at least snapshots of impacts 5 and 10 years post-effective date. Fortunately, the downstream grid and air quality models can run scenarios based on future-year assumptions. Although this is more complicated than the analysis of the first year after the standard comes into effect, it may be more feasible.
To build energy load profiles based on the regional allocation of shipments, the model follows the next steps:
c) Use RECS stock estimates to downscale national to PA (or northeast)
d) For year 1, year 5, year 10, post-effective date of the standard:
a. Calculate the difference between the base case and TSL: (NBASE,Y, EBASE,Y*) – (NTSLn,y, ETSLn,y) = Δyry, TSLn electric consumption total
The result is a model that outputs energy savings at the household level proportional to the number of units per state (in this case, Pennsylvania) based on each product class and efficiency level. This will be input into the Grid model, providing mortality and social costs at each standard level.
Accounting for electricity savings between the baseline case and each trial standard level
As mentioned previously, each trial standard level represents a regulatory energy efficiency setting for a type of appliance. As previously mentioned, the model is using RAC as the testing case. Using the shipments/stock model, the calculation averages the full-year difference between efficiency levels per product class and then aggregates them by Trial Standard Levels: Δyry, TSLn. This will be a single value by TSL for the full year, by each of the 8760 hours of the year.
Individual hourly energy consumption observations (for a full year: 8760 observations) are converted to percentage terms from metering data or ResStock to obtain a proportion of energy usage per hour. This number is multiplied by each of the 8760 hours of the year for all of PA (or northeast) for each TSL / modeled year.
This becomes the input sheet for Grid, containing a row for:
a) TSL2 yr1, TSL2 yr5, TSL2 yr10
b) TSL3 yr1, TSL3 yr5, TSL3 yr10
c) …etc
Therefore, The Grid model will produce the expected difference in regional emissions associated with selecting TSLn rather than the base case.
The Grid model is then used to obtain air quality predictions from energy demand assumptions. The output is at a regional level of CO2, SOX, and NOx estimates for each modeled TSL per year. One assumption is the current mix of renewable vs. fossil fuel-based electricity generation plants. The researchers may use a more extended timeframe for the model (for example, 30 years in the future) and use it for different regions of the U.S.
The other predictive model used will be InMap. This uses regional PM2.5 emission estimates from Grid that can be further downscaled and converted to estimated health impacts. Other folks at the Laboratory operate the predictive models that will yield the following outputs:
a) PM2.5 concentration/exposure
b) Mortality
c) Valuation of human health impacts
The Grid model required the data to be organized in horizontal rows in CSV format, by TSL, and by balancing area. There are 134 regional grid balancing areas (including power flow between balancing areas) relevant to tracing the source of energy generation. Thus, the model can calculate emissions based on the generator and its profile based on the fuel used. Pennsylvania is divided into four balancing areas, which the model divides proportionally based on population data from the DOE.
VI. Findings
The Air Quality Index Analysis model data was handed to another team of researchers who ran it through the Grid model in late April, days before this report was finalized. The results confirmed that the model worked.
The first set included health results under three grid scenarios (low, medium, and high RE cost) with the number of premature mortalities. These numbers ranged from low (based on the Krewski equation, which quantifies health risk for particulate matter[17]) to high (based on the LePuele equation, which also quantifies fine particles associated with mortality rates[18]). The outputs also included a social cost based on the value of a statistical life from the Environmental Protection Agency.
A second set of results included power plant runs. These represented annual emissions by plants for air pollutants and GHG emissions. The units for this second set were kg for all air pollutants.
The researchers noted that the InMAP results can be broken down by disadvantaged vs. non-disadvantaged communities (according to Justice40) and race-ethnicity by re-running the InMAP code with a few input changes.
This Air Quality Index Analysis proved that using energy standards as inputs and health effects as model outputs is possible. It will be the foundation of other models that can further refine findings by communities and populations.
Other components, such as a qualitative dimension linking these effects with the experience of members of these communities, need to be integrated. Once new iterations are run, better questions and better results will emerge.
VII. Conclusions
This research underscores the imperative of aligning energy policy with broader societal goals articulated in the United Nations' Sustainable Development Goals (SDGs) in navigating the multifaceted landscape of energy production and consumption and its ramifications for public health and environmental sustainability. Rooted in the foundational principles of the 2030 Agenda for Sustainable Development, this study emphasizes the interconnectedness between energy production, public health, climate action, economic growth, and social equity.
Recognizing the pivotal role of energy in driving socioeconomic development and environmental stewardship, this research articulates a nuanced understanding of the environmental and social impacts of energy production and use.
The study finds the relationship between energy efficiency standards, air quality, and public health outcomes, drawing on a rich tapestry of empirical evidence and theoretical insights. By examining the health effects of air pollution, the efficacy of efficiency standards in mitigating emissions, and the disparities in energy access and environmental justice, this study situates itself within a broader discourse on sustainable energy transitions and equitable development.
Building upon this foundation, the model built by the Economics Subgroup researchers at LBNL links the potential health impacts associated with efficiency standards-induced changes to air quality at a regional level. Furthermore, this Capstone Project underlines the importance of integrating environmental, social, and economic considerations into energy policymaking.
By embracing a multidisciplinary approach and leveraging cutting-edge methodologies, this research endeavors to inform decision-makers, stakeholders, and the public in charting a path toward a more sustainable and resilient energy future.
Future exploration must be done to connect these quantitative findings to qualitative experiences from communities around generation plants experiencing negative spillover effects and externalities from energy production. For this research project, however, quantitative analysis will be the method through which the main questions will be answered.
Appendix
Appendix Figure 1. RAC Shipments - Source: AC Last Rule
Appendix Figure 2. Status quo of energy sources for electricity generation
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Footnotes
[1] https://sdgs.un.org/goals
[2] U.S. Energy Information Administration. Electricity explained: Electricity generation, capacity, and sales in the United States, https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php
[3] According to Rapid Energy Policy Evaluation and Analysis Toolkit. https://repeatproject.org/
[4] https://energy5.com/the-role-of-energy-efficient-appliances-in-reducing-consumption
[5] Air Pollution and Your Health, National Institute of Environmental Health Services https://www.niehs.nih.gov/health/topics/agents/air-pollution
[6] Ambient (outdoor) air pollution report by World Health Organization, December 19, 2022, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
Air Pollution and Your Health, National Institute of Environmental Health Sciences. https://www.niehs.nih.gov/health/topics/agents/air-pollution
[7] Carbon Pollution from Transportation, United States Environmental Protection Agency, https://www.epa.gov/transportation-air-pollution-and-climate-change/carbon-pollution-transportation
[8] Climate Change Impacts on Air Quality, United States Environmental Protection Agency, https://www.epa.gov/climateimpacts/climate-change-impacts-air-quality
[9] Ibid.
[10] Climate Change Impacts Across California: Health, Legislative Analyst Office 2022, https://lao.ca.gov/reports/2022/4580/Climate-Change-Impacts-Health-040522.pdf
[11] Research on Health Effects from Air Pollution, United States Environmental Protection Agency 2023
https://www.epa.gov/air-research/research-health-effects-air-pollution
[12] The units for the results will be the number of premature mortalities. The results range from low (based on the Krewski equation) to high (based on the LePuele equation). These can be turned into social costs based on the value of a statistical life from the EPA. https://www.epa.gov/environmental-economics/mortality-risk-valuation
[13] Examples include: Technical Support Document: Energy Efficient Program for Consumer Products and Commercial and Industrial Equipment: Room Air Conditioners. https://www1.eere.energy.gov/buildings/appliance_standards/standards.aspx?productid=52&action=viewlive
[14] Cooling profile includes other appliances such as central (whole home) air conditioners, ceiling fans, etc.
[15] Energy Efficiency and Renewable Energy, Building Technologies Program, Appliances and Commercial Equipment Standards: Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Room Air Conditioners. https://www.energy.gov/sites/default/files/2023-03/rac-ecs-fr.pdf
[16] https://www.eia.gov/consumption/residential/data/2020/state/pdf/State%20Air%20Conditioning.pdf
[17] https://www3.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf
[18]https://wwwcdn.imo.org/localresources/en/MediaCentre/HotTopics/Documents/Finland%20study%20on%20health%20benefits.pdf