Internet of Things: Energy boon or bane? Hittinger, Eric; Jaramillo, Paulina
Science (American Association for the Advancement of Science),
2019-Apr-26, 2019-04-26, 20190426, Letnik:
364, Številka:
6438
Journal Article
Recenzirano
Networked digital devices may cause rising energy use, although devices save energy locally
Since the dawn of the internet, a digital revolution has transformed life for millions of people. Digital ...files have replaced paper, email has replaced letters, and cell phones provide access to many services that facilitate daily life. This digital revolution is not over, and there is now a growing deployment of technologies grouped under the term “Internet of Things” (IoT)—a worldwide network of interconnected objects that are uniquely addressable via standard communication protocols (
1
). By 2020, there may be as many as 30 billion objects connected to the internet (
2
), all of which require energy. These devices may yield direct energy savings (
3
,
4
), but it is much less clear what their net effect on the broader energy system will be. Scientists and regulators will need to work together to ensure that the IoT's benefits do not come at the expense of rising energy use.
This paper uses air pollution emissions data for the years 2002, 2005, 2008, and 2011 to estimate monetary damages due to air pollution exposure for PM2.5, SO2, NOx, NH3, and VOC from electric power ...generation, oil and gas extraction, coal mining, and oil refineries. In 2011, damages associated with emissions from these sectors totaled 131 billion dollars (in 2000$), with SO2 emissions from power generation being the largest contributors to social damages. Further, damages have decreased significantly since 2002, even as energy production increased, suggesting that, among other factors, policies that have driven reductions in emissions have reduced damages. The results of this analysis highlight the spatial heterogeneity of the impacts associated with the emissions of a given pollutant. In the past, environmental regulations have assumed that the benefits of air emissions reductions are homogenous across source location. This analysis suggests that policy designs that account for spatial differences in the impacts of air emissions could result in more effective environmental regulation. Accounting for such spatial heterogeneity in the benefits of policies would be akin to accounting for differences in compliances costs across states, which the EPA did when establishing the state emissions standards for the Clean Power Plan rule.
•Social costs of emissions from energy sector decreased between 2002 and 2011.•Emissions from power generation are the major contributors to social costs.•Policies to control SO2 emissions may produce the largest social costs reductions.
Net-zero emissions energy systems Davis, Steven J; Lewis, Nathan S; Shaner, Matthew ...
Science (American Association for the Advancement of Science),
2018-Jun-29, 2018-06-29, 20180629, Letnik:
360, Številka:
6396
Journal Article
Recenzirano
Odprti dostop
Some energy services and industrial processes-such as long-distance freight transport, air travel, highly reliable electricity, and steel and cement manufacturing-are particularly difficult to ...provide without adding carbon dioxide (CO
) to the atmosphere. Rapidly growing demand for these services, combined with long lead times for technology development and long lifetimes of energy infrastructure, make decarbonization of these services both essential and urgent. We examine barriers and opportunities associated with these difficult-to-decarbonize services and processes, including possible technological solutions and research and development priorities. A range of existing technologies could meet future demands for these services and processes without net addition of CO
to the atmosphere, but their use may depend on a combination of cost reductions via research and innovation, as well as coordinated deployment and integration of operations across currently discrete energy industries.
A variety of mathematical models have been proposed to characterize and quantify the dependency of electricity supply technology costs on various drivers of technological change. The most prevalent ...model form, called a learning curve, or experience curve, is a log-linear equation relating the unit cost of a technology to its cumulative installed capacity or electricity generated. This one-factor model is also the most common method used to represent endogenous technical change in large-scale energy-economic models that inform energy planning and policy analysis. A characteristic parameter is the “learning rate,” defined as the fractional reduction in cost for each doubling of cumulative production or capacity. In this paper, a literature review of the learning rates reported for 11 power generation technologies employing an array of fossil fuels, nuclear, and renewable energy sources is presented. The review also includes multi-factor models proposed for some energy technologies, especially two-factor models relating cost to cumulative expenditures for research and development (R&D) as well as the cumulative installed capacity or electricity production of a technology. For all technologies studied, we found substantial variability (as much as an order of magnitude) in reported learning rates across different studies. Such variability is not readily explained by systematic differences in the time intervals, geographic regions, choice of independent variable, or other parameters of each study. This uncertainty in learning rates, together with other limitations of current learning curve formulations, suggests the need for much more careful and systematic examination of the influence of how different factors and assumptions affect policy-relevant outcomes related to the future choice and cost of electricity supply and other energy technologies.
•We review models explaining the cost of 11 electricity supply technologies.•The most prevalent model is a log-linear equation characterized by a learning rate.•Reported learning rates for each technology vary considerably across studies.•More detailed models are limited by data requirements and verification.•Policy-relevant influences of learning curve uncertainties require systematic study.
Transportation policy and planning decisions, including decisions on new infrastructure and public transport improvements, affect local and global environmental conditions. This work studies the ...effect of increased road capacity on traffic-related emissions in Bogotá using a tool that couples a transportation model with emission factors from COPERT IV. We followed a parametric approach varying transport supply and demand, comparing three scenarios: a baseline scenario that represents the transportation system in Bogota in 2015; scenario 1 incorporates five highway capacity-enhancement projects in Bogotá and assumes insensitive travel demand; scenario 2 incorporates the new highway projects but assumes a demand increase of 13% in vehicle trips with private cars. Results include daily and annual values of traffic-related emissions of five air pollutant criteria: CO, NO
x
, PM
10
, SO
2
, and VOC for the baseline scenario, scenario 1, and scenario 2. We found a reduction in emissions after adding highway capacity and assuming inelastic demand (scenario 1). Scenario 1 results in a 15% reduction in PM
10
emissions and a 10% reduction in NO
x
emissions. In contrast, results for scenario 2 suggest increased emissions for all air pollutant criteria (e.g., VOC and CO emissions increase by 21% and 22% compared with the baseline scenario). Therefore, new traffic demand would eliminate the emission savings observed in scenario 1 and could potentially further degrade air quality in Bogotá. While an exact estimate of induced demand that may result from highway expansion in Bogotá is not available, this analysis highlights that such projects could lead to an increase in emissions unless there is a combined effort to managing demand of private vehicle trips.
Plastics production is responsible for 1% and 3% of U.S. greenhouse gas (GHG) emissions and primary energy use, respectively. Replacing conventional plastics with bio-based plastics (made from ...renewable feedstocks) is frequently proposed as a way to mitigate these impacts. Comparatively little research has considered the potential for green energy to reduce emissions in this industry. This paper compares two strategies for reducing greenhouse gas emissions from U.S. plastics production: using renewable energy or switching to renewable feedstocks. Renewable energy pathways assume all process energy comes from wind power and renewable natural gas derived from landfill gas. Renewable feedstock pathways assume that all commodity thermoplastics will be replaced with polylactic acid (PLA) and bioethylene-based plastics, made using either corn or switchgrass, and powered using either conventional or renewable energy. Corn-based biopolymers produced with conventional energy are the dominant near-term biopolymer option, and can reduce industry-wide GHG emissions by 25%, or 16 million tonnes CO2e/year (mean value). In contrast, switching to renewable energy cuts GHG emissions by 50%-75% (a mean industry-wide reduction of 38 million tonnes CO2e/year). Both strategies increase industry costs-by up to $85/tonne plastic (mean result) for renewable energy, and up to $3000 tonne−1 plastic for renewable feedstocks. Overall, switching to renewable energy achieves greater emission reductions, with less uncertainty and lower costs than switching to corn-based biopolymers. In the long run, producing bio-based plastics from advanced feedstocks (e.g. switchgrass) and/or with renewable energy can further reduce emissions, to approximately 0 CO2e/year (mean value).
The U.S. Department of Energy (DOE) estimates that in the coming decades the United States' natural gas (NG) demand for electricity generation will increase. Estimates also suggest that NG supply ...will increasingly come from imported liquefied natural gas (LNG). Additional supplies of NG could come domestically from the production of synthetic natural gas (SNG) via coal gasification−methanation. The objective of this study is to compare greenhouse gas (GHG), SO x , and NO x life-cycle emissions of electricity generated with NG/LNG/SNG and coal. This life-cycle comparison of air emissions from different fuels can help us better understand the advantages and disadvantages of using coal versus globally sourced NG for electricity generation. Our estimates suggest that with the current fleet of power plants, a mix of domestic NG, LNG, and SNG would have lower GHG emissions than coal. If advanced technologies with carbon capture and sequestration (CCS) are used, however, coal and a mix of domestic NG, LNG, and SNG would have very similar life-cycle GHG emissions. For SO x and NO x we find there are significant emissions in the upstream stages of the NG/LNG life-cycles, which contribute to a larger range in SO x and NO x emissions for NG/LNG than for coal and SNG.
Wind power introduces variability into electric power systems. Due to the physical characteristics of wind, most of this variability occurs at inter-hour time-scales and coal units are therefore ...technically capable of balancing wind. Operators of coal-fired units have raised concerns that additional cycling will be prohibitively costly. Using PJM bid-data, we observe that coal operators are likely systematically under-bidding their startup costs. We then consider the effects of a 20% wind penetration scenario in the coal-heavy PJM West area, both when coal units bid business as usual startup costs, and when they bid costs accounting for the elevated wear and tear that occurs during cycling. We conclude that while 20% wind leads to increased coal cycling and reduced coal capacity factors under business as usual startup costs, including full startup costs shifts the burden of balancing wind onto more flexible units. This shift has benefits for CO2, NOX, and SO2 emissions as well as for the profitability of coal plants, as calculated by our dispatch model.
Agriculture contributes up to 50% of the gross domestic product in some East African countries and is the backbone of the region’s economy. Most farmers rely on traditional, small-scale subsistence ...farming with low fertilizer use and low-yield seeds. Similarly, less than 3% of the total cultivated area employs any form of irrigation, mostly non-pressurized. Meanwhile, electricity providers frequently struggle with low and unpredictable demand, challenging their ability to recover rural infrastructure investments. Using electricity to pump irrigation water can increase agricultural productivity and improve the financial sustainability of rural electricity supply. This study evaluates the productive and economic feasibility of pressurized small-scale irrigation systems in Ethiopia, Rwanda, and Uganda for three staple crops and two horticulture crops. To study these effects, we develop simplified engineering-based irrigation and hydrology models and combine them with an existing biophysical crop growth model using district-level agrometeorological, soil, and crop physiology data as inputs. Our results indicate that small-scale pressurized irrigation can significantly increase yields for horticulture crops and staples such as maize or potato grown with improved seeds and moderate or greater fertility levels. The sensitivity analysis shows that irrigation may be techno-economically viable in up to 36% of Ethiopian woredas, 67% of Rwandan districts, and 45% of Ugandan districts provided the use of improved cultivars and non-limiting fertility conditions. These results highlight the value of complementing irrigation investments with electricity infrastructure in East Africa.
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