Mapping Spatiotemporal Disparities in Residential Electricity Inequality Using Machine Learning.

The move toward electrification is critical for decarbonizing the energy sector but may exacerbate energy unaffordability without proper safeguards. Addressing this challenge requires capturing neighborhood-scale dynamics to uncover the blind spots in residential electricity inequality. Based on publicly available, multisourced remote sensing and census data, we develop a high-resolution, spatiotemporally explicit machine learning (ML) framework to predict tract-level monthly electricity consumption across the conterminous U.S. from 2013-2020. We then construct the electricity affordability gap (EAG) metric, defined as the gap between electricity bills and 3% of household income, to better identify energy-vulnerable communities over space and time. The results show that our framework largely improves the resolution of electricity consumption data while achieving an R2 of 0.82 compared to the Low-Income Energy Affordability Data (LEAD). We estimate an annual $16.18 billion economic burden on the ability to afford electricity bills, exceeding current federal appropriations in alleviating energy difficulties. We also observe pronounced seasonal and urban-rural disparities, with monthly EAG in summer and winter being 2-3 times greater than other seasons and rural residents facing burdens up to 1.7 times higher than their urban counterparts. These insights inform equitable electrification by addressing spatiotemporal mismatches and multiple jurisdictional challenges in energy justice efforts.

New Insight into the Measurements of Particle-Bound Metals in the Urban and Remote Atmospheres of the Sarajevo Canton and Modeled Impacts of Particulate Air Pollution in Bosnia and Herzegovina.

The Sarajevo Canton Winter Field Campaign 2018 (SAFICA) was a project that took place in winter 2017-2018 with an aim to characterize the chemical composition of aerosol in the Sarajevo Canton, Bosnia and Herzegovina (BiH), which has one of the worst air qualities in Europe. This paper presents the first characterization of the metals in PM10 (particulate matter aerodynamic diameters ≤10 µm) from continuous filter samples collected during an extended two-months winter period at the urban background Sarajevo and remote Ivan Sedlo sites. We report the results of 18 metals detected by inductively coupled plasma mass spectrometry (ICP-MS) and electrothermal atomic absorption spectrometry (ETAAS). The average mass concentrations of metals were higher at the Sarajevo site than at Ivan Sedlo and ranged from 0.050 ng/m3 (Co) to 188 ng/m3 (Fe) and from 0.021 ng/m3 (Co) to 61.8 ng/m3 (Fe), respectively. The BenMAP-CE model was used for estimating the annual BiH health (50% decrease in PM2.5 would save 4760+ lives) and economic benefits (costs of $2.29B) of improving the air quality. Additionally, the integrated energy and health assessment with the ExternE model provided an initial estimate of the additional health cost of BiH's energy system.

How do imports and exports affect green productivity? New evidence from partially linear functional-coefficient models.

Globalization and income disparities have raised an urgent need to re-examine the environmental consequences of international trade. Using a global panel dataset covering 93 economies from 1980 to 2017, this paper explores the heterogeneous impacts of international trade on green productivity. Unlike previous studies that impose strict linear assumptions on functional forms, we adopt a newly developed partially linear functional-coefficient model to estimate the specific response functions of green productivity to imports and exports at different income levels, thus emphasizing the potential role of income heterogeneity. The results demonstrate that (1) imports and exports have different non-linear effects on green productivity; (2) imports do not significantly affect green productivity in lower-income countries (relative income level is less than 0.5), but imports increasingly promote green productivity in high-income countries; (3) exports hinder green productivity in extremely low-income countries (relative income level is less than 0.1), while gradually improving green productivity in high-income countries (relative income level is larger than 0.6); and (4) imports and exports promote green productivity more significantly by technological progress rather than efficiency improvements. The stimulus effect from induced technological progress is only observed in higher-income countries.

Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model.

While large-scale adoption of electric vehicles (EVs) globally would reduce carbon dioxide (CO2) and traditional air pollutant emissions from the transportation sector, emissions from the electric sector, refineries, and potentially other sources would change in response. Here, a multi-sector human-Earth systems model is used to evaluate the net long-term emission implications of large-scale EV adoption in the US over widely differing pathways of the evolution of the electric sector. Our results indicate that high EV adoption would decrease net CO2 emissions through 2050, even for a scenario where all electric sector capacity additions through 2050 are fossil fuel technologies. Greater net CO2 reductions would be realized for scenarios that emphasize renewables or decarbonization of electricity production. Net air pollutant emission changes in 2050 are relatively small compared to expected overall decreases from recent levels to 2050. States participating in the Regional Greenhouse Gas Initiative experience greater CO2 and air pollutant reductions on a percentage basis. These results suggest that coordinated, multi-sector planning can greatly enhance the climate and environmental benefits of EVs. Additional factors are identified that influence the net emission impacts of EVs, including the retirement of coal capacity, refinery operations under reduced gasoline demands, and price-induced fuel switching in residential heating and in the industrial sector.

Trace Metal Content of Coal Exacerbates Air-Pollution-Related Health Risks: The Case of Lignite Coal in Kosovo.

More than 6600 coal-fired power plants serve an estimated five billion people globally and contribute 46% of annual CO2 emissions. Gases and particulate matter from coal combustion are harmful to humans and often contain toxic trace metals. The decades-old Kosovo power stations, Europe's largest point source of air pollution, generate 98% of Kosovo's electricity and are due for replacement. Kosovo will rely on investment from external donors to replace these plants. Here, we examine non-CO2 emissions and health impacts by using inductively coupled plasma mass spectrometry (ICP-MS) to analyze trace metal content in lignite coal from Obilic, Kosovo. We find significant trace metal content normalized per kWh of final electricity delivered (As (22.3 ± 1.7), Cr (44.1 ± 3.5), Hg (0.08 ± 0.010), and Ni (19.7 ± 1.7) mg/kWhe). These metals pose health hazards that persist even with improved grid efficiency. We explore the air-pollution-related risk associated with several alternative energy development pathways. Our analysis estimates that Kosovo could avoid 2300 premature deaths by 2030 with investments in energy efficiency and solar PV backed up by natural gas. Energy policy decisions should account for all associated health risks, as should multilateral development banks before guaranteeing loans on new electricity projects.

Looking for medium-term conservation and development impacts of community management agreements in Uganda's Rwenzori Mountains National Park.

We evaluate the impact of collaborative management agreements (CMAs) designed to protect forests and raise incomes for smallholders living adjacent to Rwenzori Mountains National Park (RMNP), Uganda. We use a quasi-experimental study design to estimate changes in several income measures, as well as land cover using three waves (2003, 2007, and 2012) of household survey and remote sensing data. Overall, we find no significant impact of CMAs on any of our income measures. However, when disaggregating households by income quartile, we find that access to forest resources in RMNP may have had an income stabilizing effect for poor households. Forest income grew significantly faster among the poorest quartile of treatment relative to control households, partially because poor households recorded very low income from forests at baseline. The effect of CMAs on forest cover is minimal, although we find that conversion of woody savanna and savanna to cropland is more pronounced in villages with CMAs. These findings suggest that in the medium-term, CMAs have failed to deliver conservation or development benefits related to enhancing livelihoods or conserving forests near RMNP. Practitioners should consider different CMA models or other strategies for improving welfare and forest health outcomes in communities neighboring protected areas.

A measurement strategy to address disparities across household energy burdens.

Energy inequity is an issue of increasing urgency. Few policy-relevant datasets evaluate the energy burden of typical American households. Here, we develop a framework using Net Energy Analysis and household socioeconomic data to measure systematic energy inequity among critical groups that need policy attention. We find substantial instances of energy poverty in the United States - 16% of households experience energy poverty as presently defined as spending more than 6% of household income on energy expenditures. More than 5.2 million households above the Federal Poverty Line face energy poverty, disproportionately burdening Black, Hispanic, and Native American communities. For solar, wind, and energy efficiency to address socioeconomic mobility, programs must reduce energy expenditures by expanding eligibility requirements for support and access to improved conservation measures, efficiency upgrades, and distributed renewables. We recommend the United States develop a more inclusive federal energy poverty categorization that increases assistance for household energy costs.

High-resolution electricity generation model demonstrates suitability of high-altitude floating solar power.

This paper develops a meteorological site selection algorithm to quantify the electricity generation potential of floating solar design configurations on alpine water bodies in Switzerland. Using European power market demand patterns, we estimate the technical and economic potential of 82 prospective high-altitude floating solar sites co-located with existing Swiss hydropower. We demonstrate that the amount of solar energy radiating from high-altitude Swiss water bodies could meet total national electricity demand while significantly reducing carbon emissions and addressing seasonal supply/demand deficits. We construct a global map overlaying sites on each continent where high-altitude floating solar could provide low-carbon, land-sparing electricity. Our results present a compelling motivation to develop alpine floating solar installations. However, significant innovations are still needed to couple floating solar with existing hydropower operations or low-cost energy storage. As the industry matures, high-altitude floating solar technology could become a high-value, low-carbon electricity source.