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The document highlights superhot rock geothermal as a scalable, zero-emission energy source that could significantly exceed Oregon's current electricity needs. It emphasizes the technology's low land footprint and its ability to provide constant 'firm power' to the grid, supporting Oregon's goal of 100% clean energy by 2040.

The document highlights that just 1% of Russia's superhot rock geothermal resource could provide 6,704 GW of energy capacity, far exceeding the country's projected 2050 electricity demand of 1,783,308 GWh. While unlikely to meet 2030 climate goals, this technology could theoretically replace all coal and gas in Russia's electricity production, reducing emissions by approximately 834 megatonnes of CO2eq. The report emphasizes the technology's low land footprint (12 km² per 1 GW) and its ability to leverage Russia's existing oil and gas subsurface expertise for a zero-carbon transition.

The document outlines the potential for superhot rock geothermal energy in South Dakota, noting that just 1% of the state's estimated resource could provide 49 GW of capacity, which is 30 times the state's 2023 electricity consumption. It argues that superhot rock geothermal is a viable global energy source because it does not require natural underground water sources and provides carbon-free firm power with a significantly lower land footprint than solar, wind, or biomass.

The document highlights that just 1% of South Korea's superhot rock geothermal resource could provide 50 GW of capacity, meeting 68% of its 2021 electricity demand and potentially replacing 48% of its coal imports. It argues that this energy-dense, 24/7 renewable source could significantly reduce carbon emissions and land-use requirements compared to other energy technologies.

The document argues that superhot rock geothermal could be a gamechanger for Texas's energy grid, noting that just 1% of the state's resource could meet 50% of its 2023 electricity consumption. It emphasizes the technology's low land footprint and its ability to leverage existing oil and gas subsurface expertise.

The document reports that clean electricity generated 38% of the world's power in 2021, surpassing coal. While renewables met all electricity demand growth in the first half of 2022, the report notes that unabated coal power must fall by 87% in the next nine years to align with 1.5C goals. Regional analysis highlights China's leadership in wind and solar, Europe's accelerated transition due to the gas crisis, and the significant role of coal mines as the largest source of energy sector methane.

The document outlines the technical processes Ember uses to assemble electricity data for 215 countries from 2000 to 2021. It describes the use of multiple sources—including the EIA, Eurostat, and IRENA—to reconcile annual and monthly data, disaggregate thermal generation, and estimate emissions using IPCC factors. The guide also provides a detailed country-by-country breakdown of the specific sources and adjustments applied to each territory's data.

In 2021, wind and solar power reached a record 10.3% of global electricity generation, with total clean energy accounting for 38%. However, a 5.4% surge in global electricity demand—driven largely by economic growth in Asia—led to a record increase in coal power, which grew by 9.0% to reach 10,042 TWh. Consequently, power sector CO2 emissions rose by 7% in 2021, the largest percentage increase since 2010, surpassing pre-pandemic levels.

The document outlines Ember's methodology for assembling electricity data for 215 countries. It describes the use of multiple sources—including Eurostat, ENTSO-E, the Energy Institute, and the U.S. Energy Information Administration—to reconcile annual and monthly time series. The guide explains how Ember handles data gaps through ARIMA-based projections, disaggregates thermal generation using IEA and EI ratios, and calculates lifecycle emissions using IPCC and UNECE factors. It also provides a comprehensive country-by-country breakdown of the specific sources and adjustments applied to each national dataset.

The report argues that the United States can leverage its vast offshore wind potential to provide 10-25% of its total electricity generation by 2050 without significantly increasing wholesale costs. It details three ambition scenarios (Low, Medium, and High), noting that a High Ambition target of 750 GW by 2050 would result in wholesale costs only 0.25% higher than a baseline scenario. The analysis emphasizes that offshore wind complements land-based renewables, reducing the required installation rate of the latter. However, scaling the industry requires substantial capital investment—exceeding $260 billion in the supply chain through 2045—and a shift toward clustered transmission planning to reduce interconnection costs by 35%. The report also projects that a High Ambition scenario could support approximately 390,000 jobs by 2050.

The document addresses the recent surge in U.S. electricity demand driven by data centers, manufacturing, and electric vehicles. It warns that utilities are defaulting to new gas plants, which risks creating stranded assets and conflicting with climate goals. The authors propose a portfolio of alternatives, including aggressive energy efficiency, the reuse of fossil fuel interconnection sites for renewables, demand response with large customers, and grid-enhancing technologies to improve existing transmission infrastructure.

In 2025, the BRICS nations experienced their largest power capacity expansion on record, characterized by a 'split transition' where both fossil fuels and renewables reached historic highs. While 125 GW of new coal, oil, and gas capacity was added, solar and wind additions totaled 497 GW. This growth was heavily concentrated in China and India, where record renewable deployment began to reduce coal-fired generation despite continued coal capacity expansion. In contrast, other BRICS members like Russia and Iran remain overwhelmingly fossil-based, while Brazil, South Africa, Egypt, and the UAE show varying degrees of renewable growth and fossil fuel reliance.

The report identifies a significant disparity in electric productivity (GDP per kWh) across U.S. states. After normalizing for climate and economic composition, RMI finds a remaining gap of 1.2 million GWh annually. Closing this gap could reduce projected 2020 electricity demand by 34% compared to business-as-usual scenarios while maintaining economic growth.

The report highlights that without proactive efficiency measures, the adoption of cooling appliances in households at risk could require over 240 TWh of annual electricity by 2030. It argues that implementing best-available technologies (BAT) and stringent building energy codes can offset this demand, potentially saving 18–35 TWh through improved building envelopes alone.

The document describes how LEAP handles process dispatching during simulation, detailing five specific rules—PercentShare, ProportionalToCapacity, FullCapacity, MeritOrderDispatch, and RunningCost—and the data requirements necessary to enable each.

The paper presents 12 combinations of three demand and four supply scenarios to help Tamil Nadu plan its energy mix through 2030-31, emphasizing the need for storage integration in ambitious renewable pathways and careful power purchase planning given the financial state of TANGEDCO.

A study of over 2,000 households in Bangalore, India, found that behaviorally informed energy reports (HERs) featuring social norm comparisons and savings recommendations led to a 7 percent decrease in average monthly energy consumption over 12 months. While the lack of a control group prevents a robust causal connection, the results contrast with a nearly 3 percent increase in per capita energy usage across the state of Karnataka during the same period. Scaling this intervention to the entire city of Bangalore could potentially save households nearly US$60 million annually and avoid emissions from 604 million kilowatts of electricity generation.

The document outlines a data-intensive process for local governments to match electricity demand with carbon-free energy resources on an hourly basis. It identifies three essential datasets: hourly load data, hourly grid mix and emissions data, and hourly generation data from additional energy procurements. The guide details four methods for obtaining load data—ranging from actual meter readings to machine learning models—and directs users to resources like the EIA and NREL for grid and emissions information.