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Mitigation solutions are often evaluated in terms of costs and greenhouse gas reduction potentials, missing out on the consideration of direct effects on human well-being. Here, we systematically assess the mitigation potential of demand-side options categorized into avoid, shift and improve, and their human well-being links. We show that these options, bridging socio-behavioural, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 40–80% in end-use sectors. Based on expert judgement and an extensive literature database, we evaluate 306 combinations of well-being outcomes and demand-side options, finding largely beneficial effects in improvement in well-being (79% positive, 18% neutral and 3% negative), even though we find low confidence on the social dimensions of well-being. Implementing such nuanced solutions is based axiomatically on an understanding of malleable rather than fixed preferences, and procedurally on changing infrastructures and choice architectures. Results demonstrate the high mitigation potential of demand-side mitigation options that are synergistic with well-being.

Capacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180-$730 and 110-570 kgCO2 per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%-67%, while impact on emissions is mixed (-20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

Fully decarbonizing global industry is essential to achieving climate stabilization, and reaching net zero greenhouse gas emissions by 2050–2070 is necessary to limit global warming to 2 °C. This paper assembles and evaluates technical and policy interventions, both on the supply side and on the demand side. It identifies measures that, employed together, can achieve net zero industrial emissions in the required timeframe. Key supply-side technologies include energy efficiency (especially at the system level), carbon capture, electrification, and zero-carbon hydrogen as a heat source and chemical feedstock. There are also promising technologies specific to each of the three top-emitting industries: cement, iron & steel, and chemicals & plastics. These include cement admixtures and alternative chemistries, several technological routes for zero-carbon steelmaking, and novel chemical catalysts and separation technologies. Crucial demand-side approaches include material-efficient design, reductions in material waste, substituting low-carbon for high-carbon materials, and circular economy interventions (such as improving product longevity, reusability, ease of refurbishment, and recyclability). Strategic, well-designed policy can accelerate innovation and provide incentives for technology deployment. High-value policies include carbon pricing with border adjustments or other price signals; robust government support for research, development, and deployment; and energy efficiency or emissions standards. These core policies should be supported by labeling and government procurement of low-carbon products, data collection and disclosure requirements, and recycling incentives. In implementing these policies, care must be taken to ensure a just transition for displaced workers and affected communities. Similarly, decarbonization must complement the human and economic development of low- and middle-income countries.

Carbon capture (CC) technology is receiving increasing attention as a critical technology for climate change mitigation. Most previous studies focus on the application of CC technology in the power generation sector, while fewer studies have analyzed applications in the refining industry, which is one of the largest greenhouse gas (GHG) emissions sources in the U.S. industrial sector. Unlike the power generation sector, the refining industry has highly distributed CO2 emission sources. In this paper, bottom-up modeling and techno-economic analysis approaches are integrated to quantify the national CO2 emission reduction potential and costs of three types of CC technologies applied to U.S. refineries: (1) pre-combustion, (2) post-combustion, and (3) oxyfuel-combustion. Two scenarios are developed to compare different design strategies for CC systems; one is a distributed design scenario for post-combustion technology, the other is a centralized design scenario for pre-combustion and oxyfuel-combustion technology. The results of the two scenarios are compared, and the trade-offs between different design strategies are highlighted. The results shown in this study provide an intuitive and quantitative understanding of the potential of CC technology to reduce CO2 emissions from the U.S. refining industry. Such information is helpful to policymakers, oil companies, and energy/environmental analysts for strategic planning and systems design to manage future CO2 emissions of refineries.

Data centers are energy intensive buildings that have grown in size and number to meet the increasing demands of a digital economy. This paper presents a bottom-up model to estimate data center electricity demand in the United States over a 20 year period and examines observed and projected electricity use trends in the context of changing data center operations. Results indicate a rapidly increasing electricity demand at the turn of the century that has significantly subsided to a nearly steady annual electricity use of about 70 billion kWh in recent years. While data center workloads continue to grow exponentially, comparable increases in electricity demand have been avoided through the adoption of key energy efficiency measures and a shift towards large cloud-based service providers. Alternative projections from the model illustrate the wide range in potential electricity that could be consumed to support data centers, with the US data center workload demand estimated for 2020 requiring a total electricity use that varies by about 135 billion kWh, depending on the adoption rate of efficiency measures during this decade. While recent improvements in data center energy efficiency have been a success, the growth of data center electricity use beyond 2020 is uncertain, as modeled trends indicate that the efficiency measures of the past may not be enough for the data center workloads of the future. The results show that successful stabilization of data center electricity will require new innovations in data center efficiency to further decouple electricity demand from the ever-growing demand for data center services.

Effective energy policies rely on credible and comprehensive national energy data systems, in developed and developing economies alike. Smart system technologies will play a central role in the clean energy transition—including applications in smart homes, factories, transport systems, and renewable electricity grids—and their ability to compile and communicate point-of-use energy data presents new opportunities for improving national energy data systems. This paper reviews the growing importance of energy data systems for energy policy in the developing country context, identifies key characteristics a national data system needs to have in order to be robust and viable, discusses the potential role of smart system technologies in national energy data systems moving forward, and recommends several future research areas to better understand their potential, and in developing countries in particular.

Implementation of carbon capture and sequestration (CCS) will increase water demand due to the cooling water requirements of CO2 capture equipment. If the captured CO2 is injected into saline aquifers for sequestration, brine may be extracted to manage the aquifer pressure, and can be desalinated to provide additional freshwater supply. We conduct a geospatial analysis to determine how CCS may affect local water supply and demand across the contiguous United States. We calculate baseline indices for each county in the year 2005, and project future water supply and demand with and without CCS through 2030. We conduct sensitivity analyses to identify the system parameters that most significantly affect water balance. Water supply changes due to inter-annual variability and projected climate change are overwhelmingly the most significant sources of variation. CCS can have strong local effects on water supply and demand, but overall it has a modest effect on water balances.

The U.S. EPA is in the final stages of promulgating regulations to reduce CO2 emissions from the electricity generating industry. A major component of EPA's regulatory strategy targets improvements to power plant operating efficiencies. As the EPA expands regulatory requirements to other industries, including petroleum refining, it is likely that plant efficiency improvements will be critical to achieving CO2 emission reductions. This paper identifies efficiency improvement measures applicable to refining, and quantifies potential cost of conserved energy for these measures. Analysis is at the U.S. petroleum refining sector national-level employing an aggregated notional refinery model (NRM), with the aim of estimating the efficacy of efficiency improvements for reducing emissions. Using this method, roughly 1500 petajoules per year (PJ/yr) of plant fuel savings and 650 gigawatt-hour per year (GWh/yr) of electricity savings (representing 54% and 2% of U.S. refining industry consumption, respectively) are potentially cost-effective. This equates to a potential 85 Mt-CO2/yr reduction. An additional 458 PJ/yr fuel reduction and close to 2750 GWh/yr of electricity reduction (27 Mt-CO2/yr) are not cost-effective at prevailing natural gas market prices. Results are presented as a supply-curve ordering measures from low to high cost of fuel savings versus cumulative energy reduction.

Data centers are a significant and growing component of electricity demand in the United States. This paper presents a bottom up model that can be used to estimate total data center electricity demand within a region as well as the potential electricity savings associated with energy efficiency improvements. The model is applied to estimate 2008 U.S. data center electricity demand and the technical potential for electricity savings associated with major measures for IT devices and infrastructure equipment. Results suggest that 2008 demand was approximately 69 billion kilowatt-hours (1.8percent of 2008 total U.S. electricity sales) and that it may be technically feasible to reduce this demand by up to 80percent (to 13 billion kilowatt-hours) through aggressive pursuit of energy efficiency measures. Measure-level savings estimates are provided, which shed light on the relative importance of different measures at the national level. Measures applied to servers are found to have the greatest contribution to potential savings.

This paper summarizes a modeling framework that characterizes the key underlying technologies and processes that contribute to the supply chain energy use and greenhouse gas (GHG) emissions of a variety of goods and services purchased by U.S. consumers. The framework couples an input-output supply chain modeling approach with "bottom-up" fuel end use models for individual IO sectors. This fuel end use modeling detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the supply chain energy and GHG "footprints" of goods and services. To illustrate the policy-relevance of this approach, a case study was conducted to estimate achievable household GHG footprint reductions associated with the adoption of best practice energy-efficient supply chain technologies.

The term ?household carbon footprint? refers to the total annual carbon emissions associated with household consumption of energy, goods, and services. In this project, Lawrence Berkeley National Laboratory developed a carbon footprint modeling framework that characterizes the key underlying technologies and processes that contribute to household carbon footprints in California and the United States. The approach breaks down the carbon footprint by 35 different household fuel end uses and 32 different supply chain fuel end uses. This level of end use detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the carbon footprint of California households. The modeling framework was applied to estimate the annual home energy and supply chain carbon footprints of a prototypical California household. A preliminary assessment of parameter uncertainty associated with key model input data was also conducted. To illustrate the policy-relevance of this modeling framework, a case study was conducted that analyzed the achievable carbon footprint reductions associated with the adoption of energy efficient household and supply chain technologies.

The U.S. fruit and vegetable processing industry--defined in this Energy Guide as facilities engaged in the canning, freezing, and drying or dehydrating of fruits and vegetables--consumes over $800 million worth of purchased fuels and electricity per year. Energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. There are a variety of opportunities available at individual plants in the U.S. fruit and vegetable processing industry to reduce energy consumption in a cost-effective manner. This Energy Guide discusses energy efficiency practices and energy-efficient technologies that can be implemented at the component, process, facility, and organizational levels. A discussion of the trends, structure, and energy consumption characteristics of the U.S. fruit and vegetable processing industry is provided along with a description of the major process technologies used within the industry. Next, a wide variety of energy efficiency measures applicable to fruit and vegetable processing plants are described. Many measure descriptions include expected savings in energy and energy-related costs, based on case study data from real-world applications in fruit and vegetable processing facilities and related industries worldwide. Typical measure payback periods and references to further information in the technical literature are also provided, when available. Given the importance of water in fruit and vegetable processing, a summary of basic, proven measures for improving plant-level water efficiency are also provided. The information in this Energy Guide is intended to help energy and plant managers in the U.S. fruit and vegetable processing industry reduce energy and water consumption in a cost-effective manner while maintaining the quality of products manufactured. Further research on the economics of all measures--as well as on their applicability to different production practices--is needed to assess their cost effectiveness at individual plants.

With the emergence of extended producer responsibility regulations for electronic devices, it is becoming increasingly important for electronics manufacturers to apply design for recycling (DFR) methods in the design of plastic enclosures. This paper presents an analytical framework for quantifying the environmental and economic benefits of DFR for plastic computer enclosures during the design process, using straightforward metrics that can be aligned with corporate environmental and financial performance goals. The analytical framework is demonstrated via a case study of a generic desktop computer enclosure design, which is recycled using a typical US "take-back" system for plastics from waste electronics. The case study illustrates how the analytical framework can be used by the enclosure designer to quantify the environmental and economic benefits of two important DFR strategies: choosing high-value resins and minimizing enclosure disassembly time. Uncertainty analysis is performed to quantify the uncertainty surrounding economic conditions in the future when the enclosure is ultimately recycled.

Personal computers (PCs) are one of the most ubiquitous and indispensable electronic devices in use within California’s homes and businesses. More PCs are estimated to be in use in California than in any other U.S. state. According to the latest published estimates from U.S. Department of Energy (DOE), 11.5 million PCs were installed in California’s homes (as of 2001) and 6.5 million PCs were installed in California’s commercial buildings (as of 2003) (U.S. DOE 2003, 2006a). Based on these DOE data and recent PC sales data, it was estimated in this research project that as of 2005 nearly 19 million PCs were installed in California homes and businesses and that this number is expected to grow significantly through 2012.This research project focused on evaluating a set of realistic measures aimed at reducing the life-cycle energy use and GHG emissions associated with operating and maintaining California’s residential and commercial PC stock (hereafter referred to simply as “California’s PC stock”). The term “maintain” is used here to describe the ongoing process of replacing, upgrading, and discarding obsolete PCs within California’s PC stock as necessary. The project employed a life-cycle assessment (LCA) approach to characterize both direct and indirect energy use and GHG emissions. Direct energy use and GHG emissions were defined as the energy use and GHG emissions attributable to the operational electricity consumption of California’s PC stock. Indirect energy use and GHG emissions were defined as the energy use and GHG emissions attributable to activities that support ongoing PC stock maintenance, namely, the manufacture of new PCs and PC components as needed and the end-of-life treatment (i.e., waste disposal and recycling) of obsolete PCs and PC components.In this research project, the potential reductions in life-cycle energy use and GHG emissions associated with the measures considered were projected through the year 2012. The purpose of this work was to characterize the effectiveness of these measures to inform PC-related policies for near-term energy efficiency improvements and GHG emission reductions in the State of California. Baseline projections were first established for the life-cycle energy use and GHG emissions associated with operating and maintaining California’s PC stock from 2005 to 2012. These baseline projections were made using best-available data from public sources for all key modeling parameters. Whenever possible, ranges for key modeling parameters were established to account for published data variations. Two future scenarios were considered: a low energy use and GHG emissions scenario (“low scenario”) and a high energy use and GHG emissions scenario (“high scenario”). The purpose of the two-scenario approach was to establish preliminary upper and lower bounds on the results based on the feasible data ranges identified from the literature for key modeling parameters. The potential reductions in life-cycle energy use and GHG emissions associated with the measures considered were then projected for each scenario over the same time period.Commercial PC estimate was derived by: (1) assuming that 8.6 million PCs were installed in commercial buildings in the Pacific Census Region in 2003, based on the U.S. DOE 2003 Commercial Building Energy Use Survey (CBECS) (U.S. DOE 2006), and (2) assuming that 75% of Pacific Census Region commercial sector employment (as defined by the 2003 CBECS) and hence PC usage occurs in California based on employment data from the U.S. Census Bureau (U.S. Census Bureau 2006).

Whereas Part I of this article ("Improving Energy Efficiency in Pharmaceutical Manufacturing Operations ? Part I: Motors, Drives and Compressed Air Systems", Pharmaceutical Manufacturing, Feb. 2006) focused on motors, drives and compressed air systems, Part II will review, briefly, potential improvements in heating, ventilation and air conditioning (HVAC) systems, overall building management and boilers. Research in this article was first published last September, in an extensive report developed by the Energy Analysis Department at Lawrence Berkeley National Laboratories for the Environmental Protection Agency?s Energy Star Pharmaceutical Focus. The 90-page guide, ?Energy Efficiency Improvement and Cost Saving Opportunities for the Pharmaceutical Industry,? is available in pdf format at www.energystar.gov.The U.S. pharmaceutical industry spent nearly $900 million on energy in 2002. As energy costs increase, more companies are looking into energy efficiency measures Considered individually, each measure may offer small savings, but combined they add up to significant savings and short payback periods.

Product life-cycle optimization addresses the reduction of environmental burdens associated with the production, use, and end-of-life stages of a product s life cycle. In this paper, we offer an evaluation of the opportunities related to product life-cycle optimization in California for two key products: personal computers (PCs) and concrete. For each product, we present the results of an explorative case study to identify specific opportunities for greenhouse gas (GHG) emissions reductions at each stage of the product life cycle. We then offer a discussion of the practical policy options that may exist for realizing the identified GHG reduction opportunities. The case studies demonstrate that there may be significant GHG mitigation options as well as a number of policy options that could lead to life-cycle GHG emissions reductions for PCs and concrete in California.

Analysts assessing energy policies and energy modelers forecasting future trends need to have access to reliable and concise energy statistics. Lawrence Berkeley National Laboratory evaluated several sources of California energy data, primarily from the California Energy Commission and the U.S. Energy Information Administration, to develop the California Energy Balance Database (CALEB). This database manages highly disaggregated data on energy supply, transformation, and end-use consumption for each type of energy commodity from 1990 to the most recent year available (generally 2001) in the form of an energy balance, following the methodology used by the International Energy Agency. This report presents the data used for CALEB and provides information on how the various data sources were reconciled. CALEB offers the possibility of displaying all energy flows in numerous ways (e.g., physical units, Btus, petajoules, different levels of aggregation), facilitating comparisons among the different types of energy commodities and different end-use sectors. In addition to displaying energy data, CALEB can also be used to calculate state-level energy-related carbon dioxide emissions using the methodology of the Intergovernmental Panel on Climate Change.

This paper presents an overview of the modeling approach and highlights key findings of a California industrial energy efficiency potential study. In addition to providing estimates of technical and economic potential, the study examines achievable program potential under various program-funding scenarios. The focus is on electricity and natural gas savings for manufacturing in the service territories of California's investor-owned utilities (IOUs). The assessment is conducted by industry type and by end use. Both crosscutting technologies and industry-specific process measures are examined. Measure penetration into the marketplace is modeled as a function of customer awareness, measure cost effectiveness, and perceived market barriers. Data for the study comes from a variety of sources, including: utility billing records, the Energy Information Association (EIA) Manufacturing Energy Consumption Survey (MECS), state-sponsored avoided cost studies, energy efficiency program filings, and technology savings and cost data developed through Lawrence Berkeley National Laboratory (LBNL). The study identifies 1,706 GWh and 47 Mth (million therms) per year of achievable potential over the next twelve years under recent levels of program expenditures, accounting for 5.2 percent of industrial electricity consumption and 1.3 percent of industrial natural gas consumption. These estimates grow to 2,748 GWh and 192 Mth per year if all cost-effective and achievable opportunities are pursued. Key industrial electricity end uses, in terms of energy savings potential, include compressed air and pumping systems that combine to account for about half of the total achievable potential estimates. For natural gas, savings are concentrated in the boiler and process heating end uses, accounting for over 99 percent to total achievable potential.